Multivariate analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "72891c7e",
   "metadata": {},
   "source": [
    "# MULTIVARIATE ANALYSIS CODE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7e718c1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries used for multivariate analysis\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib  import pyplot\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "import zipfile\n",
    "import statsmodels.stats.diagnostic as smd\n",
    "from statsmodels.stats.stattools import durbin_watson\n",
    "from statsmodels.nonparametric.smoothers_lowess import lowess\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "import warnings\n",
    "from scipy.stats import gmean\n",
    "from geopandas import gpd\n",
    "import rasterio\n",
    "from rasterio.transform import Affine\n",
    "from rasterio.enums import Resampling\n",
    "import rioxarray\n",
    "from functools import partial\n",
    "from rasterio.enums import MergeAlg\n",
    "from osgeo import gdal\n",
    "from osgeo import ogr\n",
    "from osgeo import osr\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.gridspec import GridSpec as gspec\n",
    "\n",
    "# Fonr used for most figures\n",
    "font = {'family':'Bahnschrift', 'fontsize' : 16}\n",
    "# Ignore warnings while running this code (It is recommended to uncomment this line)\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c84321d",
   "metadata": {},
   "source": [
    "## Radon concentrations analysis\n",
    "We import the radon concentrations measured. (RAW DATA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7a5b860",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_muestra</th>\n",
       "      <th>dias_muestreo</th>\n",
       "      <th>Concentracion_Bq_m3</th>\n",
       "      <th>Contramuestra</th>\n",
       "      <th>Resultado</th>\n",
       "      <th>Error_perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_casa</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>35</td>\n",
       "      <td>175.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>46.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.860</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>81.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "      <td>173.91</td>\n",
       "      <td>180.26</td>\n",
       "      <td>177.085</td>\n",
       "      <td>3.585849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>35</td>\n",
       "      <td>107.13</td>\n",
       "      <td>130.00</td>\n",
       "      <td>118.565</td>\n",
       "      <td>19.288998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>61.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "      <td>40.75</td>\n",
       "      <td>50.00</td>\n",
       "      <td>45.375</td>\n",
       "      <td>20.385675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>15</td>\n",
       "      <td>36</td>\n",
       "      <td>182.30</td>\n",
       "      <td>170.00</td>\n",
       "      <td>176.150</td>\n",
       "      <td>6.982685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>15.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>129.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.940</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>145.00</td>\n",
       "      <td>135.00</td>\n",
       "      <td>140.000</td>\n",
       "      <td>7.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>198.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>25.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.750</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>107.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.130</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "      <td>102.66</td>\n",
       "      <td>95.00</td>\n",
       "      <td>98.830</td>\n",
       "      <td>7.750683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>17</td>\n",
       "      <td>36</td>\n",
       "      <td>50.60</td>\n",
       "      <td>30.80</td>\n",
       "      <td>40.700</td>\n",
       "      <td>48.648649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>82.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>237.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>237.010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>26</td>\n",
       "      <td>34</td>\n",
       "      <td>159.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>24</td>\n",
       "      <td>35</td>\n",
       "      <td>140.00</td>\n",
       "      <td>120.30</td>\n",
       "      <td>130.150</td>\n",
       "      <td>15.136381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "      <td>102.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.660</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>12</td>\n",
       "      <td>35</td>\n",
       "      <td>134.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>134.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>36.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.050</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>25.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.750</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>417.00</td>\n",
       "      <td>397.00</td>\n",
       "      <td>407.000</td>\n",
       "      <td>4.914005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>25</td>\n",
       "      <td>34</td>\n",
       "      <td>102.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.600</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>9</td>\n",
       "      <td>36</td>\n",
       "      <td>77.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.200</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>195.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>195.700</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>41.20</td>\n",
       "      <td>37.00</td>\n",
       "      <td>39.100</td>\n",
       "      <td>10.741688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>22</td>\n",
       "      <td>33</td>\n",
       "      <td>108.18</td>\n",
       "      <td>97.85</td>\n",
       "      <td>103.015</td>\n",
       "      <td>10.027666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         ID_muestra  dias_muestreo  Concentracion_Bq_m3  Contramuestra  \\\n",
       "ID_casa                                                                  \n",
       "1                20             35               175.00            NaN   \n",
       "10                3             35                46.86            NaN   \n",
       "16                7             35                81.80            NaN   \n",
       "17               16             36               173.91         180.26   \n",
       "19               19             35               107.13         130.00   \n",
       "22                4             35                61.80            NaN   \n",
       "24               14             36                40.75          50.00   \n",
       "26               15             36               182.30         170.00   \n",
       "28                5             35                15.40            NaN   \n",
       "29               27             34               129.94            NaN   \n",
       "30               28             35               145.00         135.00   \n",
       "36               30             34               198.00            NaN   \n",
       "37                2             35                25.75            NaN   \n",
       "38               29             34               107.13            NaN   \n",
       "41               21             34               102.66          95.00   \n",
       "43               17             36                50.60          30.80   \n",
       "48                6             34                82.40            NaN   \n",
       "49               18             35               237.01            NaN   \n",
       "50               26             34               159.00            NaN   \n",
       "53               24             35               140.00         120.30   \n",
       "58               13             33               102.66            NaN   \n",
       "60               12             35               134.00            NaN   \n",
       "66                1             34                36.05            NaN   \n",
       "69               23             35                25.75            NaN   \n",
       "72                8             35               417.00         397.00   \n",
       "73               25             34               102.60            NaN   \n",
       "74                9             36                77.20            NaN   \n",
       "76               11             36               195.70            NaN   \n",
       "78               10             36                41.20          37.00   \n",
       "79               22             33               108.18          97.85   \n",
       "\n",
       "         Resultado  Error_perc  \n",
       "ID_casa                         \n",
       "1          175.000         NaN  \n",
       "10          46.860         NaN  \n",
       "16          81.800         NaN  \n",
       "17         177.085    3.585849  \n",
       "19         118.565   19.288998  \n",
       "22          61.800         NaN  \n",
       "24          45.375   20.385675  \n",
       "26         176.150    6.982685  \n",
       "28          15.400         NaN  \n",
       "29         129.940         NaN  \n",
       "30         140.000    7.142857  \n",
       "36         198.000         NaN  \n",
       "37          25.750         NaN  \n",
       "38         107.130         NaN  \n",
       "41          98.830    7.750683  \n",
       "43          40.700   48.648649  \n",
       "48          82.400         NaN  \n",
       "49         237.010         NaN  \n",
       "50         159.000         NaN  \n",
       "53         130.150   15.136381  \n",
       "58         102.660         NaN  \n",
       "60         134.000         NaN  \n",
       "66          36.050         NaN  \n",
       "69          25.750         NaN  \n",
       "72         407.000    4.914005  \n",
       "73         102.600         NaN  \n",
       "74          77.200         NaN  \n",
       "76         195.700         NaN  \n",
       "78          39.100   10.741688  \n",
       "79         103.015   10.027666  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import dataframe\n",
    "DF = pd.read_excel('Dataset for fitting/Raw_Results_LR115.xlsx')\n",
    "\n",
    "#Drop NA data\n",
    "DF_data = DF[(DF['Concentracion_Bq_m3'].notna())] \n",
    "\n",
    "# Calculate Percentage error\n",
    "DF_data['Error_perc'] = DF_data.apply(lambda df: 100*np.abs(df['Concentracion_Bq_m3'] - df['Contramuestra'])/df['Resultado'], axis = 1) #Add error column\n",
    "# Get ID of sample as numeric\n",
    "DF_data.ID_muestra = DF_data.ID_muestra.apply(lambda df : int(str(df)[:2]))\n",
    "\n",
    "# Set house ID as index\n",
    "DF_data = DF_data.set_index('ID_casa')\n",
    "\n",
    "DF_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc252d1",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Import data of all the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d1f50e55-666a-44e6-a58f-792879996ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dataset with preprocessed data (Radon concentrations and Predictors (Gathered from multiple sources (Geologic data, meterologic data, Survey)))\n",
    "DF_RC = pd.read_csv('Dataset for fitting/Processed_DataFrame.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "68d0d6fc-1b36-4c15-a1f4-5063784f0e98",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>Basement_dummy</th>\n",
       "      <th>Age_1980</th>\n",
       "      <th>Lito_Fans</th>\n",
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       "      <th>0</th>\n",
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       "      <td>99.511871</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>5534.072234</td>\n",
       "      <td>2.032655</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>14.303343</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4887.241543</td>\n",
       "      <td>1.896419</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>14.291571</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5678.445117</td>\n",
       "      <td>1.948309</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>14.112653</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10815.009970</td>\n",
       "      <td>2.074108</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.145676</td>\n",
       "      <td>14.316550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6204.616747</td>\n",
       "      <td>1.940533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.318565</td>\n",
       "      <td>14.302312</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5032.376765</td>\n",
       "      <td>1.939020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.467979</td>\n",
       "      <td>14.409709</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13435.781270</td>\n",
       "      <td>2.777478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.990311</td>\n",
       "      <td>14.347118</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12960.805490</td>\n",
       "      <td>2.674097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.523292</td>\n",
       "      <td>14.299123</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4997.736385</td>\n",
       "      <td>2.025850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.232109</td>\n",
       "      <td>14.037408</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8627.384258</td>\n",
       "      <td>2.038902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116.932610</td>\n",
       "      <td>14.130319</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8880.562833</td>\n",
       "      <td>2.120338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.679855</td>\n",
       "      <td>14.257227</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6758.584502</td>\n",
       "      <td>2.099211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.442856</td>\n",
       "      <td>14.309039</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4349.131167</td>\n",
       "      <td>1.888139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.595619</td>\n",
       "      <td>14.264681</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6964.609725</td>\n",
       "      <td>2.014935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.157211</td>\n",
       "      <td>14.231512</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7854.774253</td>\n",
       "      <td>2.069222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.179352</td>\n",
       "      <td>14.357324</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11286.031440</td>\n",
       "      <td>3.163036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.424751</td>\n",
       "      <td>14.274158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6488.057194</td>\n",
       "      <td>2.309927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.752434</td>\n",
       "      <td>14.384271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6318.240032</td>\n",
       "      <td>2.094390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.019707</td>\n",
       "      <td>14.131492</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7664.211273</td>\n",
       "      <td>2.097234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.539902</td>\n",
       "      <td>15.145882</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5997.273477</td>\n",
       "      <td>2.746446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119.114441</td>\n",
       "      <td>14.406137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9195.844687</td>\n",
       "      <td>3.119258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.048927</td>\n",
       "      <td>14.402822</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9624.791476</td>\n",
       "      <td>2.427195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.504936</td>\n",
       "      <td>14.397980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8674.266123</td>\n",
       "      <td>3.318798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.858566</td>\n",
       "      <td>14.290498</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8496.503796</td>\n",
       "      <td>2.662393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.791275</td>\n",
       "      <td>14.323051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4803.180864</td>\n",
       "      <td>2.271906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.051323</td>\n",
       "      <td>14.171663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7491.004812</td>\n",
       "      <td>2.069232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97.616234</td>\n",
       "      <td>14.406315</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.923422</td>\n",
       "      <td>1.997061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.475410</td>\n",
       "      <td>13.702313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>742.988099</td>\n",
       "      <td>1.541482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.497086</td>\n",
       "      <td>13.703671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>559.092224</td>\n",
       "      <td>1.772805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.354630</td>\n",
       "      <td>14.299008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5126.100945</td>\n",
       "      <td>1.968092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Basement_dummy  Age_1980  Lito_Fans  Lito_Shales  Precipitation  \\\n",
       "0              0.0       0.0        0.0          0.0      99.511871   \n",
       "1              1.0       0.0        0.0          0.0     103.417252   \n",
       "2              1.0       0.0        0.0          0.0     105.484779   \n",
       "3              0.0       1.0        0.0          0.0     118.937675   \n",
       "4              0.0       0.0        0.0          0.0     100.145676   \n",
       "5              0.0       0.0        0.0          0.0     103.318565   \n",
       "6              0.0       0.0        0.0          0.0     111.467979   \n",
       "7              0.0       1.0        0.0          0.0     112.990311   \n",
       "8              0.0       0.0        0.0          0.0     104.523292   \n",
       "9              0.0       1.0        0.0          0.0     120.232109   \n",
       "10             0.0       1.0        0.0          0.0     116.932610   \n",
       "11             1.0       1.0        0.0          0.0     108.679855   \n",
       "12             0.0       0.0        1.0          0.0     102.442856   \n",
       "13             0.0       0.0        0.0          0.0     108.595619   \n",
       "14             1.0       1.0        0.0          0.0     110.157211   \n",
       "15             0.0       0.0        0.0          0.0     137.179352   \n",
       "16             0.0       1.0        0.0          0.0     105.424751   \n",
       "17             0.0       1.0        0.0          0.0      90.752434   \n",
       "18             0.0       0.0        0.0          0.0     115.019707   \n",
       "19             0.0       1.0        0.0          0.0      71.539902   \n",
       "20             0.0       0.0        0.0          0.0     119.114441   \n",
       "21             1.0       0.0        0.0          0.0      94.048927   \n",
       "22             0.0       0.0        0.0          0.0     108.504936   \n",
       "23             0.0       1.0        0.0          0.0     104.858566   \n",
       "24             1.0       1.0        0.0          0.0     103.791275   \n",
       "25             0.0       1.0        0.0          0.0     113.051323   \n",
       "26             0.0       0.0        0.0          1.0      97.616234   \n",
       "27             1.0       0.0        0.0          0.0      72.475410   \n",
       "28             0.0       0.0        1.0          0.0      72.497086   \n",
       "29             0.0       0.0        0.0          0.0     105.354630   \n",
       "\n",
       "    Temperature  Urban_dummy  Fault_proximity  Vertical_velocity  \n",
       "0     14.321247          1.0      5534.072234           2.032655  \n",
       "1     14.303343          1.0      4887.241543           1.896419  \n",
       "2     14.291571          1.0      5678.445117           1.948309  \n",
       "3     14.112653          1.0     10815.009970           2.074108  \n",
       "4     14.316550          1.0      6204.616747           1.940533  \n",
       "5     14.302312          1.0      5032.376765           1.939020  \n",
       "6     14.409709          1.0     13435.781270           2.777478  \n",
       "7     14.347118          1.0     12960.805490           2.674097  \n",
       "8     14.299123          1.0      4997.736385           2.025850  \n",
       "9     14.037408          1.0      8627.384258           2.038902  \n",
       "10    14.130319          1.0      8880.562833           2.120338  \n",
       "11    14.257227          1.0      6758.584502           2.099211  \n",
       "12    14.309039          1.0      4349.131167           1.888139  \n",
       "13    14.264681          1.0      6964.609725           2.014935  \n",
       "14    14.231512          1.0      7854.774253           2.069222  \n",
       "15    14.357324          1.0     11286.031440           3.163036  \n",
       "16    14.274158          1.0      6488.057194           2.309927  \n",
       "17    14.384271          1.0      6318.240032           2.094390  \n",
       "18    14.131492          1.0      7664.211273           2.097234  \n",
       "19    15.145882          1.0      5997.273477           2.746446  \n",
       "20    14.406137          0.0      9195.844687           3.119258  \n",
       "21    14.402822          0.0      9624.791476           2.427195  \n",
       "22    14.397980          0.0      8674.266123           3.318798  \n",
       "23    14.290498          1.0      8496.503796           2.662393  \n",
       "24    14.323051          1.0      4803.180864           2.271906  \n",
       "25    14.171663          1.0      7491.004812           2.069232  \n",
       "26    14.406315          0.0        97.923422           1.997061  \n",
       "27    13.702313          0.0       742.988099           1.541482  \n",
       "28    13.703671          0.0       559.092224           1.772805  \n",
       "29    14.299008          1.0      5126.100945           1.968092  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24c79deb-fb46-4d28-86f3-ee53dedf7d61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Basement_dummy</th>\n",
       "      <th>Age_1980</th>\n",
       "      <th>Lito_Fans</th>\n",
       "      <th>Lito_Shales</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Urban_dummy</th>\n",
       "      <th>Fault_proximity</th>\n",
       "      <th>Vertical_velocity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.511871</td>\n",
       "      <td>14.321247</td>\n",
       "      <td>1</td>\n",
       "      <td>5534.072234</td>\n",
       "      <td>2.032655</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103.417252</td>\n",
       "      <td>14.303343</td>\n",
       "      <td>1</td>\n",
       "      <td>4887.241543</td>\n",
       "      <td>1.896419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105.484779</td>\n",
       "      <td>14.291571</td>\n",
       "      <td>1</td>\n",
       "      <td>5678.445117</td>\n",
       "      <td>1.948309</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>118.937675</td>\n",
       "      <td>14.112653</td>\n",
       "      <td>1</td>\n",
       "      <td>10815.009970</td>\n",
       "      <td>2.074108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.145676</td>\n",
       "      <td>14.316550</td>\n",
       "      <td>1</td>\n",
       "      <td>6204.616747</td>\n",
       "      <td>1.940533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103.318565</td>\n",
       "      <td>14.302312</td>\n",
       "      <td>1</td>\n",
       "      <td>5032.376765</td>\n",
       "      <td>1.939020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111.467979</td>\n",
       "      <td>14.409709</td>\n",
       "      <td>1</td>\n",
       "      <td>13435.781270</td>\n",
       "      <td>2.777478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112.990311</td>\n",
       "      <td>14.347118</td>\n",
       "      <td>1</td>\n",
       "      <td>12960.805490</td>\n",
       "      <td>2.674097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>104.523292</td>\n",
       "      <td>14.299123</td>\n",
       "      <td>1</td>\n",
       "      <td>4997.736385</td>\n",
       "      <td>2.025850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120.232109</td>\n",
       "      <td>14.037408</td>\n",
       "      <td>1</td>\n",
       "      <td>8627.384258</td>\n",
       "      <td>2.038902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>116.932610</td>\n",
       "      <td>14.130319</td>\n",
       "      <td>1</td>\n",
       "      <td>8880.562833</td>\n",
       "      <td>2.120338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.679855</td>\n",
       "      <td>14.257227</td>\n",
       "      <td>1</td>\n",
       "      <td>6758.584502</td>\n",
       "      <td>2.099211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>102.442856</td>\n",
       "      <td>14.309039</td>\n",
       "      <td>1</td>\n",
       "      <td>4349.131167</td>\n",
       "      <td>1.888139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.595619</td>\n",
       "      <td>14.264681</td>\n",
       "      <td>1</td>\n",
       "      <td>6964.609725</td>\n",
       "      <td>2.014935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110.157211</td>\n",
       "      <td>14.231512</td>\n",
       "      <td>1</td>\n",
       "      <td>7854.774253</td>\n",
       "      <td>2.069222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137.179352</td>\n",
       "      <td>14.357324</td>\n",
       "      <td>1</td>\n",
       "      <td>11286.031440</td>\n",
       "      <td>3.163036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105.424751</td>\n",
       "      <td>14.274158</td>\n",
       "      <td>1</td>\n",
       "      <td>6488.057194</td>\n",
       "      <td>2.309927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90.752434</td>\n",
       "      <td>14.384271</td>\n",
       "      <td>1</td>\n",
       "      <td>6318.240032</td>\n",
       "      <td>2.094390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115.019707</td>\n",
       "      <td>14.131492</td>\n",
       "      <td>1</td>\n",
       "      <td>7664.211273</td>\n",
       "      <td>2.097234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71.539902</td>\n",
       "      <td>15.145882</td>\n",
       "      <td>1</td>\n",
       "      <td>5997.273477</td>\n",
       "      <td>2.746446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>119.114441</td>\n",
       "      <td>14.406137</td>\n",
       "      <td>0</td>\n",
       "      <td>9195.844687</td>\n",
       "      <td>3.119258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.048927</td>\n",
       "      <td>14.402822</td>\n",
       "      <td>0</td>\n",
       "      <td>9624.791476</td>\n",
       "      <td>2.427195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.504936</td>\n",
       "      <td>14.397980</td>\n",
       "      <td>0</td>\n",
       "      <td>8674.266123</td>\n",
       "      <td>3.318798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>104.858566</td>\n",
       "      <td>14.290498</td>\n",
       "      <td>1</td>\n",
       "      <td>8496.503796</td>\n",
       "      <td>2.662393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103.791275</td>\n",
       "      <td>14.323051</td>\n",
       "      <td>1</td>\n",
       "      <td>4803.180864</td>\n",
       "      <td>2.271906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113.051323</td>\n",
       "      <td>14.171663</td>\n",
       "      <td>1</td>\n",
       "      <td>7491.004812</td>\n",
       "      <td>2.069232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>97.616234</td>\n",
       "      <td>14.406315</td>\n",
       "      <td>0</td>\n",
       "      <td>97.923422</td>\n",
       "      <td>1.997061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.475410</td>\n",
       "      <td>13.702313</td>\n",
       "      <td>0</td>\n",
       "      <td>742.988099</td>\n",
       "      <td>1.541482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>72.497086</td>\n",
       "      <td>13.703671</td>\n",
       "      <td>0</td>\n",
       "      <td>559.092224</td>\n",
       "      <td>1.772805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105.354630</td>\n",
       "      <td>14.299008</td>\n",
       "      <td>1</td>\n",
       "      <td>5126.100945</td>\n",
       "      <td>1.968092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Basement_dummy  Age_1980  Lito_Fans  Lito_Shales  Precipitation  \\\n",
       "0                0         0          0            0      99.511871   \n",
       "1                1         0          0            0     103.417252   \n",
       "2                1         0          0            0     105.484779   \n",
       "3                0         1          0            0     118.937675   \n",
       "4                0         0          0            0     100.145676   \n",
       "5                0         0          0            0     103.318565   \n",
       "6                0         0          0            0     111.467979   \n",
       "7                0         1          0            0     112.990311   \n",
       "8                0         0          0            0     104.523292   \n",
       "9                0         1          0            0     120.232109   \n",
       "10               0         1          0            0     116.932610   \n",
       "11               1         1          0            0     108.679855   \n",
       "12               0         0          1            0     102.442856   \n",
       "13               0         0          0            0     108.595619   \n",
       "14               1         1          0            0     110.157211   \n",
       "15               0         0          0            0     137.179352   \n",
       "16               0         1          0            0     105.424751   \n",
       "17               0         1          0            0      90.752434   \n",
       "18               0         0          0            0     115.019707   \n",
       "19               0         1          0            0      71.539902   \n",
       "20               0         0          0            0     119.114441   \n",
       "21               1         0          0            0      94.048927   \n",
       "22               0         0          0            0     108.504936   \n",
       "23               0         1          0            0     104.858566   \n",
       "24               1         1          0            0     103.791275   \n",
       "25               0         1          0            0     113.051323   \n",
       "26               0         0          0            1      97.616234   \n",
       "27               1         0          0            0      72.475410   \n",
       "28               0         0          1            0      72.497086   \n",
       "29               0         0          0            0     105.354630   \n",
       "\n",
       "    Temperature  Urban_dummy  Fault_proximity  Vertical_velocity  \n",
       "0     14.321247            1      5534.072234           2.032655  \n",
       "1     14.303343            1      4887.241543           1.896419  \n",
       "2     14.291571            1      5678.445117           1.948309  \n",
       "3     14.112653            1     10815.009970           2.074108  \n",
       "4     14.316550            1      6204.616747           1.940533  \n",
       "5     14.302312            1      5032.376765           1.939020  \n",
       "6     14.409709            1     13435.781270           2.777478  \n",
       "7     14.347118            1     12960.805490           2.674097  \n",
       "8     14.299123            1      4997.736385           2.025850  \n",
       "9     14.037408            1      8627.384258           2.038902  \n",
       "10    14.130319            1      8880.562833           2.120338  \n",
       "11    14.257227            1      6758.584502           2.099211  \n",
       "12    14.309039            1      4349.131167           1.888139  \n",
       "13    14.264681            1      6964.609725           2.014935  \n",
       "14    14.231512            1      7854.774253           2.069222  \n",
       "15    14.357324            1     11286.031440           3.163036  \n",
       "16    14.274158            1      6488.057194           2.309927  \n",
       "17    14.384271            1      6318.240032           2.094390  \n",
       "18    14.131492            1      7664.211273           2.097234  \n",
       "19    15.145882            1      5997.273477           2.746446  \n",
       "20    14.406137            0      9195.844687           3.119258  \n",
       "21    14.402822            0      9624.791476           2.427195  \n",
       "22    14.397980            0      8674.266123           3.318798  \n",
       "23    14.290498            1      8496.503796           2.662393  \n",
       "24    14.323051            1      4803.180864           2.271906  \n",
       "25    14.171663            1      7491.004812           2.069232  \n",
       "26    14.406315            0        97.923422           1.997061  \n",
       "27    13.702313            0       742.988099           1.541482  \n",
       "28    13.703671            0       559.092224           1.772805  \n",
       "29    14.299008            1      5126.100945           1.968092  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in ['RC', 'ID']:\n",
    "    if i in DF_RC.columns:\n",
    "        DF_RC = DF_RC.drop(i, axis = 1) \n",
    "DF_RC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7913e8e",
   "metadata": {},
   "source": [
    "### Basic statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c834ff0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Names of selected variables for Statistical analysis \n",
    "Variables = [\n",
    "             'Basement dummy',\n",
    "             'House age (1980 dummy)',\n",
    "             'Lithology Q-ca dummy',\n",
    "             'Lithology k1k6-Stm dummy',\n",
    "             'Precipitation [mm]',\n",
    "             'Temperature [°C]',\n",
    "             'Urban dummy',\n",
    "             'Fault proximity',\n",
    "             'Vertical velocity'\n",
    "            ]\n",
    "\n",
    "# Subsetting only the variables (Dependent and Independent)\n",
    "DF_RC = DF_RC.iloc[:,1:]\n",
    "\n",
    "# Create correlation matrix\n",
    "plt.figure(figsize = (10,10))\n",
    "sns.heatmap(DF_RC.corr(), vmax=1, vmin=-1, center=0, cmap='seismic', annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82e28945",
   "metadata": {},
   "source": [
    "### Independent variable caracterization\n",
    "\n",
    "The following lines of code create a summary plot for all of the independent variables for this specific dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d2fdcd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2BAwdWUFBgc11P/vss3KDvVQ7KvI61md1ZT8HDRoEZ2dnGAwGHDhwADt37kR6ejrkcjnGjBmDiIgIdOnSBUDRyN+CggJp0r4HHnigNrtOVC4GfImIiIiIiKhe8/Pzk0YMXrx4sdR6hYWFeP311/Haa69h5cqV0nLz0XM5OTlW1zUtd3Z2tkeXy+Xi4iL9fezYMYiiWOrj448/trldU7DXy8sLu3btgkajsWhr0qRJdt+X8o6v+bKaOL7m28jKyrJpncrug/nruHTp0jJfxxMnTlRoP6qiMsegLKb9HDlyZJn7KIoivLy8qrw9e3B1dUX//v0BFOXx/fXXXwEU5Rw25TQeO3YsAOC3337DoUOHoNVq4ezsjIEDB9ZKn4lsxYAvERERERER1WuCIKB79+4AgC1btiAhIcFqvTVr1mDx4sVYunSpRU5Wf39/aXK106dPl1ivoKAAV65cAQAEBwfbu/tWmefxPHTokN3aTUxMBFA0urF///5Wb623N9OkZID143vq1Cnp75o4vub9MU3WV5F1KrIPfn5+cHV1BWDf17GqKnMMymI6X48fPy5NblcfmFIz7Ny5E//9738B/C/Ia/pbEATcvn1bmiixf//+0mtKVFcx4EtERERERET1nmlkqlqtxpgxY5CSkmJRvnHjRkyfPh1AUc7SyZMnS2UKhQJdu3YFAGzfvh0bN26UykRRxDvvvCON4OzVq1d17oakc+fO0kjIBQsW4MyZMxblR48exeTJk61OrmYK4lobierp6QkAOHHiRIlyg8FQ5ghp0yjXik7o1rt3b+nv9957D6mpqdLz1NRUvP/++9Lznj17Vqjtyujbty8EQQAAzJ8/H9evX7coX7t2LUaOHGmReuDee++VRsV+9tlnFuvk5uYiJiZGem5+jsjlcvTt2xcA8J///Adr16612FZSUhImT55c5nGvDqaRrQDw2muvWbxfRFHEsmXLMGXKFBgMBpvau++++wAA8fHxmDVrFrRarVRWWFiImJgY/Pbbb/bpvJH5qOvKTjJoSs1w5MgRi3QOJk2aNEG3bt0AFP2YZL5OZfqal5dXqX4SVRQnbSMiIiIiIqJ6b/LkyVi1ahX27duH2NhYNGvWDEOGDIGXlxdOnjxpcbv84sWLS4wknTlzJg4cOAAAePDBBzFgwAA0adIEJ06ckEZvOjs745lnnqmR/VEqlZg5cyYWLFiAlJQUdOrUCf3790fTpk1x8+ZN7Nq1C3q9Hjdu3MBff/1lkTogPDwcV65cwcmTJzFlyhS0bNkSb7zxBoCiYOfx48dx/fp1REVFYcCAAfD29kZOTg6OHDkijWS2pnHjxrh8+TJOnjyJqVOnon379pg5c2a5+xIVFYURI0Zg8+bNuHDhAlq3bo0BAwYAAHbt2oW0tDQAwNChQ9GiRYuqHDabREREYMyYMfjtt99w+/ZttG3bFgMGDECjRo1w4sQJHD9+HEDR6M9t27bByckJ7u7ueOqpp/Dll18iJSUFHTp0wMCBA+Hm5oa9e/fizp07AIBWrVqVuN1/1qxZ2Lp1K0RRxPjx47F06VK0bdsW2dnZ+Pvvv5GTk4O///4be/fuRbNmzap9/4GitAX33nsvjh49ipMnT6Jly5bo168f/Pz8cODAAVy6dAlA0Y8A33//fbntPf3001i8eDHu3r2Lzz77DBs3bkS3bt2gUCiwe/du3L59GwqFAkqlEqNGjbLLPpiPgo+JicGWLVswc+ZMNG/e3OY2wsPD0bp1a5w/fx4AMGDAAPj7+1vUeeyxxxAbGys9r8yEbaa+pqWlYfz48WjRogXmz59f4XaIbCYSEREREREROYDk5GSxT58+IgCrDycnJ3Hx4sWlrv/cc8+Vuq5cLhd/+OGHEuvMnTtXqhMXF1dm/3bt2iXVXblyZbn7o9VqxQcffLDUPslkMvGNN94Q9Xq9xXo//PCDRb0OHTpIZQkJCWJISEipbapUKhGA2LRp0xL9+eqrryzq9uvXz6K8X79+pa4bHx8vRkVFlbrdyMhI8fbt2yXWa9q0qdVt2bLNsqSnp4sdO3Ys89i+//77Fuvk5OSIXbp0KXUdf39/8dSpU1a39+GHH4qCIJS67vDhw8WMjAyLdeLi4qTyuXPnVmr/yzp+165dE8PDw0vtk1KptDjny+vP9u3bRQ8Pj1Lbi4qKEs+ePWuxzsqVK6XyXbt2lWizrPdMYWGh2KRJE4ttWGujPLNnz5bW/+abb0qU37lzR3rt2rRpU2o7Zb0WW7Zssehn8TqTJk2SyqypyOcMkSiKIlM6EBERERERkUMIDAzEzp078cMPP+C+++5Do0aNoFKpEB4ejqeeegpHjx7FrFmzSl3/yy+/xK+//oqBAwfCx8cHCoUCjRo1wtixY3Ho0CE8+eSTNbg3gJOTE9avX4/Vq1dj8ODB8PPzg0KhQFhYGCZNmoRjx47hgw8+gExm+V/7CRMmYPHixYiIiJBSF5g0atQIx48fx4wZM9CiRQuoVCp4enqie/fu+P333zFu3LhS+/P0008jJiYGTZs2LdFueUJCQnDkyBHExMSgTZs2cHFxgYuLC1q3bo233noLR48eRVhYWIXarApfX18cPHgQS5cuRbdu3eDh4QGlUokmTZpg4sSJiI2NxZw5cyzWcXd3x969e7FkyRJ06tQJbm5uUKlUiIqKwowZM3Dy5Em0b9/e6vZmz56N/fv347HHHkNoaCgUCgX8/f0xYsQIrF+/Hps3b4a3t3cN7Pn/REZG4vjx45g7dy46dOgANzc3ODs7o1mzZpg2bRrOnDlToXN+4MCBOHPmDF588UW0aNECzs7O8PDwQPfu3fHpp5/i7NmzaNOmjd36r1AosGHDBvTu3Rtubm6Vbsc0YtfJyQkPP/xwifLQ0FApTUdlRvcCRaPXP/30U0RFRZV4vxJVB0EURbG2O0FEREREREREREREVcefFYiIiIiIiIiIiIgcBAO+RERERERERERERA6CAV8iIiIiIiIiIiIiB8GALxEREREREREREZGDYMCXiIiIiIiIiIiIyEEw4EtERERERERERETkIBjwJSIiIiIiIiIiInIQDPgSEREREREREREROQgGfImIiIiIiIiIiIgcBAO+REREREREREQN0Lx58yAIQqkPHx8f9OjRA1999RX0en257Z06dQrPPPMMWrZsCVdXVwQFBWHw4MH46aefIIpiDewREQEM+BIRERERERERVZkpSDp58uQKr7tq1Spp/d27d9u9b5WVmZmJ2NhYPPfccxg8eDDUanWpdT/88EN06tQJ3377LS5fvoyCggKkpKRg+/bteOKJJzBw4EBkZGTUYO+JGi6n2u4AERERERERERHVrvvvvx/h4eEWy1JTU7F161ZkZWVh165dmD17Nj777LMS665YsQJvvPGG9LxHjx5o06YNkpKSsH37dqjVauzatQsjR47E7t274eTEcBRRdeI7jIiIiIiIiIiogfv8889LBHwBIC4uDp06dUJmZia+/fZbLFy4EF5eXlJ5dnY2XnnlFQCAi4sL1q1bh/vvv18qv3XrFh555BEcOXIEBw4cwNdff43p06dX+/4QNWRM6UBERERERERERFZFRETgmWeeAQAUFBTg6NGjFuW///47cnNzAQALFiywCPYCQJMmTbBx40b4+PgAAJYvX14DvSZq2BjwJSIiIiIiImrgzPPPZmRkYPr06WjUqBG8vLzQtWtXrF69usSkW/v27cPDDz+M4OBgKJVKhIWF4ZlnnkF8fHyp28nKysI777yD6OhoeHh4QKlUIjg4GMOGDcMXX3whBQ7NhYeHQxAE9O/fH3l5eXjttdfQuHFjuLi4oF27dvjss8/KnBDs9OnTmDBhAsLCwqBSqRAUFITx48fj4sWLZR6Ty5cv4//+7/8QFRUFFxcXeHl5oVOnTliwYAGysrKkeqb8uybff/99hXL59u/fH1OmTJGeDxgwoNRcvgkJCZg5cyZatGgBFxcXeHh44N5778WSJUtQUFBg0/YqIyIiQvo7NTXVouzAgQMAis6hp556yur6QUFB+OKLL/Dqq69i5MiRyM/Pr7a+EhFTOhARERERERGRUU5ODvr27YuzZ89Ky44cOYJffvkFEyZMkJYtW7YMr732mkWgNT4+Ht9++y3++OMP7Nu3Dy1btrRoOy0tDT169MDVq1ctlicnJ2Pr1q3YunUr5s+fj6+//hqjR48u0bfCwkIMGjQIsbGx0rKzZ89i5syZuHbtGj799NMS6/z666+YMGECtFqttCwlJQVr167Fhg0bsG3bNvTq1avEehs2bMC4ceMsgqhqtRonTpzAiRMn8K9//Qvbt29HmzZtrB7H6nDq1CkMHjy4RMD12LFjOHbsGH799Vfs3LkTHh4edt92XFyc9HdAQIBFWWJiIoCioK6vr2+pbYwfPx7jx4+3e9+IqCSO8CUiIiIiIiIiAEW351+8eBEjRozAhAkTEBkZCT8/P6xYsUKqc+jQIcyaNUsK9nbv3h2TJ09G165dARSNAJ00aVKJUbeLFy+Wgr2+vr549NFH8cwzz2DIkCFwdXWV1jUPzpo7dOgQjhw5gmHDhmHKlClo3769VLZ8+XKcPn3aon5cXBymTJkitde+fXtMmjQJ/fv3BwDk5+fjiSeeKDEy9s6dO3j88cel5eHh4Rg/fjwef/xxBAYGAigKUh86dAgA0KpVqxI5aadPn45BgwaVepzNjR49Gm5ubtLzNm3aYPr06QgNDZWWGQwGjBs3Tgr2hoWF4fHHH8eDDz4Id3d3AMDRo0fx2muv2bTNioiLi8O3334LAHB2dkbnzp0tyk2jslUqld23TUSVwxG+RERERERERCTZuXMn+vTpAwDQaDQ4f/48goODpfKlS5fCYDAAAH744Qc8+eSTUtl7772HmJgYHD58GPv27UPfvn0t2gWKcrqeOnUK3t7eUll2dja++eYbqNVqjB07ttS+/fHHH1KOWFEU8eKLL+KLL76AKIpYt26dRRD4iy++kFIHLFy4EDExMVLZmjVrMGHCBNy8eRPr1q3DxIkTpbKvv/4aeXl5AIApU6bgq6++glKpBFCUw/a1115Dr1698PjjjwMAunXrhm7duuGLL74AAEyaNAmff/552QfZzMyZM+Hl5SWldfj888+loLTJ33//LaWg6NevH/78808p0Hv9+nV0794dqampWLVqFT766KNKjfJ94YUXSkzalpaWhi1btkgpLKZOnWrxuhFR3cSALxEREREREREBANq1aycFe4GiUZvR0dHSc1EUsWPHDgDA0KFDLYK9ADBnzhx88cUXSExMxObNmy0CvtnZ2QCATp06lQgaenp6ljs6NTAw0GJCMEEQ8N577+HLL7+EKIo4d+6cRf3t27cDKBqB++abb1qUPfHEE1i+fDkOHz6MzZs3WwR89+7dCwBwc3PDZ599JgV7AcDFxUUK7NYkU55cAHj//felYC8AREZG4qWXXsJbb72FwsJCHD16FAMGDKjwNjZt2lRmed++fbFkyZIKt0tENY8BXyIiIiIiIiICAHTs2LHM8rt37yIzMxMAsHXrVrzwwgsl6sjlcgAoEYBt2rQprly5gj///BMvvPAC7r33XjRq1AhBQUEIDg62GEVsTatWrUos8/LyQlBQEJKSkiwmUgOAa9euAQAuXLiAGTNmlFjXFIAu3s+kpCQAQMuWLS0Cq7UpOTlZ+tt8FLO1Zab+29Py5csxbdo0ODkxjERUH/CdSkREREREREQAAJms7Kl+TPlaTcoa7Zqenm7xfPr06di+fTt0Op3V9Tp06ICZM2dKqQ1sZcodq9frS+1rRfqp0WgAoFomP6ssU58AWA1Cmy8zr1sRcXFxFikdxowZg99//x0AEBUVxWAvUT3CSduIiIiIiIiIyO6KT4b20EMPYfPmzRg5cqTVPLCnTp3CU089hV9++aWGelikeD+pyNy5cyEIAgBY5D8uzhRsrmygmYjsjwFfIiIiIiIiIrKJi4uL9PfSpUshimKpjxMnTpRYf/jw4diwYQMyMjKQlZWF69ev459//sGHH34ojdRdsWKF1W2bJoorTqvVAvhfKonifX3xxRfL7GdGRobFes7OzgBQIkVEbTIdGwDIyckpUW6+zNT/qmrfvj1Gjx4NADh27BjWr19vtV6jRo0AACkpKSWOpbk1a9bgtddew6xZs6TJ9IioejDgS0REREREREQ28fPzg6urKwDg0KFDFVpXFEWL556enoiIiECXLl0we/ZsDB8+HABw+/Ztq+ufP3++xLKcnBwkJiYCKMrna65x48YAgNjY2Ar10xTAvHz5stXgam0w9QkATp8+XaL81KlT0t/l5UKuCPNRvu+8847VoHvPnj0BFAXkV65cabWdjIwMTJ8+HUuXLsXGjRulc4iIqgcDvkRERERERERkE7lcjr59+wIA/vOf/2Dt2rUW5UlJSZg8eTIuXrxYYt2YmBgMHz4cly9fLlGWnZ2NkydPAigZuDVJT0+3SPcgiiLefvttKZDcrl07i/r33XcfAODIkSNYsmSJRcA5Ozsbzz//PPbv319iO/379wcA5OfnY8aMGSgsLJTK8vLyMGHCBHz77bcl1lMqlQBK5jm2hfkIXmvr9+7dW/r7rbfesqhz/fp1fPbZZ1I7nTt3rvD2S2M+yvfs2bNW022MGTNGSuvw9ttvY8uWLRbl8fHxGD58uDRi2toEekRkX4JY/Cc2IiIiIiIiImpQTKM4J02ahFWrVpVZd+fOnRg0aJAUQL333nvRtm1bZGdn4++//0ZOTg5CQkKwd+9eNGvWDACwYcMGPPjggwCKJoa777770KNHD7i5uSEhIQHr16+XRvbGxMRg4cKF0vbCw8Nx8+ZNad3BgwcjNDQUx44dk0a2CoKAM2fOoE2bNtJ6Fy9eRMeOHaXcsq1bt0anTp2g0+mwfft2pKWlwcPDA3///Te6desmrZeYmIiWLVtKo3vDw8PRo0cPCIKAbdu2IS0tDYIgYMWKFZg6daq0XlRUFK5duwY3NzdMmDABvXv3xoQJE2w6/vv370efPn0AFE1e17t3b8ycORPNmzcHUBTcbt++Pc6ePQsACAsLQ9++fZGfn4/t27dLAeD/+7//w1dffWXTNgFg3rx5mD9/PoCSk7aZnD59Gh07doQoimjRogXOnz9fIn3GihUr8Oyzz0rPe/bsidatWyMhIQE7duyQXoNevXph9+7dnACOqLqJRERERERERNSgARABiJMmTbKp/ocffigKgiCtV/wxfPhwMSMjQ6qfkZEhPvzww6XWNz06dOggZmVlWWyradOmIgCxR48eYps2bayu9/rrr1vt5+rVq0WlUlnq9rp06SLevn27xHp//vmn6OLiUup6QUFB4qlTpyzWeeONNyzq2HosRVEUCwsLxSZNmlisv2vXLos6Z86cEQMDA0vtU+fOncXs7GybtymKojh37lxp/bi4uFLrmb92//73v63Wee+990SZTFZq//r3729xThBR9WFKByIiIiIiIiKqkNmzZ2P//v147LHHEBoaCoVCAX9/f4wYMQLr16/H5s2b4e3tLdX39vbGf/7zHxw8eBDPPvssWrVqBQ8PD8jlcvj6+qJ3795YtmwZYmNj4enpaXWbSqUSO3fuxKRJkxAQEABnZ2dER0fjm2++waJFi6yu88QTT+DYsWN46qmnEB4eDpVKBW9vbwwYMAArV65EbGwswsLCSqx3//334+TJk3j22WcRGRkJlUoFDw8PdOzYEXPnzsXFixfRvn17i3XefvttTJs2zSLfrq0UCgU2bNiA3r17w83NzWqdtm3b4sSJE5g5cyaioqKgUqng5uaGTp064cMPP8S+ffvg4eFR4W3bwjyX74IFCyzSXJi8+eabOHLkCJ566ilERUXBxcUF/v7+uO+++/DDDz9g586dFucEEVUfpnQgIiIiIiIiojrLlNKhX79+2L17d213h4iozuMIXyIiIiIiIiIiIiIHwYAvERERERERERERkYNgwJeIiIiIiIiIiIjIQTDgS0REREREREREROQgGPAlIiIiIiIiIiIichCCKIpibXeCiIiIiIiIiIiIiKrOqbY74MgMBgMSEhLg4eEBQRBquztERFQKURSRk5ODkJAQyGS8+cUaXtOIiOoPXteIiIgaNgZ8q1FCQgIaN25c290gIiIb3b59G2FhYbXdjTqJ1zQiovqH17VyjBpV2z0gIiqyYUNt94AcDAO+1cjDwwMAsG/fPri7u9dyb6pHSkoKnn76aRQWFtZ2VyAIAupChhKlUolvv/0WgYGBtd0VsqPaOtdr47xuiOdwbm4u+vTpI31uU0kN4ZpmUleubbV9XWuInwWOjOd1kYZyXvO6RkRE1LAx4FuNTLe8uru7O+yXrYSEBBQWFmL58uWIioqq1b44OTlBp9PVah+uXr2KF198ETqdzmFf84aqts71mj6vG/o5zFQFpWsI1zSTunJtq83rWkP/LHBEPK8b5nnN6xoREVHDxIAv2UVUVBTatWtXq32oCwFfcnw1fa7zvCaqPbV9beP7n6oDz2siIiIix8cM/kREREREREREREQOggFfIiIiIiIiIiIiIgfBgC8RERERERERERGRg2DAlxqcuLg4jB8/HmFhYQgICEDfvn3x119/lah34cIFPPTQQwgODkZoaChGjRqFs2fP1kKPiSpHoVCU+liwYEFtd4+I7OTs2bPo0qULFAoFxowZY1GWn5+PmJgYtGnTBl5eXujYsSOWLVvGHKpUL5R1btv6fY6IiIioIeKkbdSgpKamok+fPkhOToaHhwfc3d1x6NAhjBo1CmvWrMHYsWMBADdv3kTfvn2RmZkJf39/aLVa/PXXXzh48CBOnz6NkJCQWt4TItvIZDI0b95cep6cnIzMzEzI5fJa7BUR2cu1a9fQvXt3aDSaEmUGgwHDhw/HwYMH4eTkhODgYFy4cAGvv/46Tpw4gR9//LEWekxkm7LObVu/zxERERE1VBzhSw3K4sWLkZycjEceeQSJiYm4desWli9fDgD45JNPpHrvvvsuMjMzMXPmTMTHxyMhIQGPP/44srKy8N1339VS74kqzsPDA2fPnpUeppnZu3btWss9IyJ7yMnJQVBQEGbNmlWibNeuXTh48CAaNWqECxcuIC4uDocOHYKrqyvWrl2Lq1ev1kKPiWxT1rlt6/c5IiIiooaKAV+qdmvXroVCocB3332H3bt3Y+TIkQgICEBQUBCeeOIJpKam1lhf7t69i549e2LevHlQqVQAgAkTJgAAbty4IdXz8vLC+PHjERMTA5lMBicnJzzxxBMAgMuXL9dYf6n+qUvne7NmzRARESE9z8zMxKFDh+Dm5oa+ffvWWD+IHFFdea9HRkbi9OnTGDp0aImyS5cuAQDGjh2L8PBwAECnTp0wZMgQALyekXX14dy29fscERERUUPFgC9Vu1OnTgEAtm/fjtGjR8PLywtTp05F48aN8euvv2LmzJk11pfvvvsOe/bsQcuWLaVlFy9eBAA0atRIWvbRRx/hhx9+gLe3t7SsoKAAAODu7l4znaV6qS6d7xcvXsSRI0ek59u2bYNOp8OAAQOk/yATUeXUlfe6p6cn3NzcrJaZrmH5+fkWy/Py8izKiczVh3Pb1u9zRERERA0Vc/hStTP9x+Hs2bM4ceKENMpIrVajZcuW2L59e5nr2zq5lEwmg8FgwIwZMyr0n9gPP/wQwP9GhliTnJyMzz77DDKZrMx6RPY+303ndWkqcr5v3rwZADB8+HCb6hNR6Wri2mb+/q/otQ0ARowYAX9/f/z00094+OGH0aNHD2zYsAE7duzAPffcg27dulWoPWoYqvvcLn5dq8y5bY0t3+eIiIiIGgoGfKnanT59GoIg4KeffpL+0wAAzs7OaNKkCU6fPl3m+gsXLqzQ9iZOnGjzfxzWrFmD//73v4iIiMDzzz9vtY6/vz+ysrIQGBiIn3/+Gb169apQf6hhqavnu8FgwNatWwEw4EtkD3X1vW7O29sbv/32GwYNGmTxvvf19cX69es5eSNZVR/O7eJs+T5HRERE1JAw4EvVKikpCcnJyejZs6c0WZS569evW+QYtUar1dq0LScnJ+h0Opv7duHCBUyfPh1yuRwrV64s9Rb35s2b48aNG0hNTcXq1asxePBgeHh42Lwdajiq43yv6Hldmn/++QdpaWlo06YNGjduXOX2iBqymrq2VfX9r9Vq8fbbb0On00GpVCIoKAgJCQm4e/cu3nvvPaxcubLSbZNjqolz217XNRNbv88RERERNSTM4UvV6uTJkwCAgQMHlihLSEhASkoKOnToUMO9KspnOH78eOTl5SEmJqbMUbuHDh1CYmIi5syZg40bN+Ltt9+uwZ5SfVJXz3cA+OuvvwAU3eJNRFVTl9/r5r799lvs27cPffr0wa1bt3D9+nVcunQJLVu2xOrVq6VR/0Qm9eXcNqnI9zkiIiKihoQjfKlamfLARUdHlyg7ceIEAKBjx45ltlEdOXxfeOEFnDt3Dn369MGcOXNsav+VV17B+++/z/8gU6mq43y3Vw5f5u8lsp+aurZVNYevKdfqm2++CT8/PwBA06ZN8dJLL+G5557Dtm3bMHTo0Aq1SY6tJs5te+bwrcz3OSIiIqKGgAFfqlam/zhY+8+Brf9xsHcuuJUrV+LHH3+Ej48Pvv/++xI5DDMzM/Hll1/Cx8cHzz33nLTcyclJKieypi6e70DRqKyTJ0/C29sbPXr0qFD7RFRSXX2vF1dYWAigKMBmThAEAIBGo6lQe+T46su5DZT/fY6IiIioIWPAl6rVqVOn4O/vbzVnqOk/DuXdGmjPHL5nz57FzJkzAQDffPON1X55eHhg+fLlSE9PR1hYGEaOHAlRFPHZZ58BAKKiomzqDzU81XG+2yPXoSmdw+DBg6UfLoio8mrq2lbV93/Hjh2xZcsWfPDBB4iOjoaPjw9u3ryJjz/+GADQqVOnSrdNjqkmzm17XNds+T5HRERE1JAxhy9Vm/z8fFy9erXUkSAnTpxA06ZN4evrW2N9mjNnDgoKCuDk5ISYmBi0bdvW4gEAcrkcCxcuhCiKePjhh9G4cWM0atQI77zzDgDg1VdfrbH+Uv1RF893E1PAd9iwYTW+bSJHU5ff68VNnz4doaGh2L17N8LCwtCsWTO0bNkSly5dQvv27TF+/Pja7iLVIfXp3Lbl+xwRERFRQ8ahXlRtTp8+DYPBYDUPXHp6Om7fvo1Ro0bVaJ/y8/MBADqdDpcuXSq13tNPPw0fHx98/PHHOHPmDJydndGnTx+8+eabGDRoUE11l+qRuni+A0W3dO/YsQOCIDDgS2QHdfW9bk1wcDAOHjyIuXPnYuvWrUhKSkJYWBgefPBBvPPOO3BxcantLlIdUp/ObVu/zxERERE1VAz4UrXp3r17qbf1+fn52ZyqwZ527Nhhc90xY8ZgzJgx1dgbciR18XwHAKVSiYyMjFrZNpEjqqvv9X79+lnddkhICFasWFELPaL6pj6d2xX5PkdERETUEDGlAxEREREREREREZGDYMCXiIiIiIiIiIiIHEZcXBxWrFiBZ555Bh06dICTkxMEQcC7775bpXYPHTqEBx98EAEBAXBxcUHr1q2xcOFCqNVqO/XcPpjSgYiIiIiIiIiIiBzGp59+ik8//dSuba5ZswaTJk2CXq9HaGgoGjdujLNnz+Kdd97Bxo0bsXv3bri6utp1m5XFEb5ERERERERERETkMPz9/fHAAw9gwYIF+Ouvv6o8R9ONGzcwdepU6PV6LF68GLdv38bx48dx5coVtGzZEkeOHMHs2bPt1Puq4whfIiIiIiIiIiIichgxMTEWz9euXVul9pYsWQKNRoMhQ4Zg1qxZ0vKmTZvi3//+N3r16oVvvvkGb7/9NoKCgqq0LXvgCF8iIiIiIiIiIiIiK0RRxPr16wEAU6dOLVHes2dP3HPPPdBqtfjjjz9quntWMeBLREREREREREREZMWtW7eQmJgIAOjVq5fVOqblhw8frrF+lYUpHYiIiIiIiIiIiKjO0mg00Gg0FstUKhVUKlW1b/vKlSvS9kJCQqzWiYyMtKhb2xjwJbu4evVqbXcBTk5O0Ol0tdqHunAcqHrV9Gtc0+c1z2Gi/6nt90NtXtdqe9+p+tT2a8vzmoiIqPrMF4Ta7kK1EefOxfz58y2WzZ07F/Pmzav2bWdkZAAAvL29IZRyjH18fCzq1jYGfKlKvLy8oFKp8OKLL9Z2VyCTyWAwGGq7G1CpVPDy8qrtbpCd1da5XhvnNc9haujqyrWttq9r/CxwLDyvi/C8JiIiqp/mzJmDV155xWJZTYzuBQC1Wg0AUCqVpdYx9aWgoKBG+lQeBnypSoKDg/HTTz8hKyurVvshCALCwsJw584diKJYq33x8vJCcHBwrfaB7K82zvXaOq95DlNDVxeubXXhusbPAsfC87oIz2siIqL6qabSN1jj7OwMACgsLCy1jindhIuLS430qTwM+FKVBQcH1/oXZ0EQEBkZCTc3t1oP+JLjqulznec1Ue2p7Wsb3/9UHXheE5G5G/n5iNixw2qZl5MT7vX2xpyoKAwMCKjhnpG5j65dw6zz5zEpLAyroqNruztEDZIpXUNmZiZEUbSa1sGUysFUt7bJarsDRERERERERFR3ZOl02JGWhiGxsdiWklLb3amXJp84AWHjRnx07Vptd4UaEJkDP2pT8+bNARSN4k1ISLBa5/r16xZ1a1ttHzMiIirFmTNnMGnSJJw5c6a2u0JERFRlvK4R1U1OgoCCESOkR9awYTjcuzd6+PjAAGD+5cu13UUiolrVpEkT6S6pAwcOWK1jWt6tW7ca61dZGPAlIqqDCgsLsXbtWqSkpGDt2rVl5goiIiKq63hdI6q7VDIZnOVy6eGpUKCrjw++bNcOAHC8ludrISKqbYIgYPTo0QCA7777rkT5wYMHcfHiRSgUCowaNaqmu2cVA75ERHXQtm3bpIl1srKysG3btlruERERUeXxukZU/6hkReECb4XCYrlBFPH1jRvosncv3DZvhtvmzei5fz/WJyaWaEMURXweF4foPXvgumkTXDZtQsudO/HCmTO4nJtbov7l3Fw8duwY/LdsgWrTJrTbvRv/vnWrRD3zdAlf37iByB074LJpE3rt34+jmZkAgF1pabh37144b9qEe3buxA+3b1vdz0S1Gk+fOoVG27ZBtWkTWuzcicVXr8JQLNf4vEuXIGzciBfOnMHviYlou3s3nDdtQtvdu/GfYrd4P/TPP/j+zh0AwKzz5yFs3IhMrbaUI/0//2RkoPf+/XDetAlN/v4bS0tJB7E7LQ3Cxo1ou3t3ibLw7dshbNwoHQd7Hq/+Bw9C2LgR6xISsPDyZYRs2wbXTZswPDYWV/PyAADrEhLQatcuOG/ahOg9e/BXcrJFG2127YKwcSO2p6ZaLNcZDPDbsgXCxo24ZOXcIKoJn3zyCcLDwzFu3LgSZbNmzYJSqcS2bduwZMkSaT6Cmzdv4qmnngIAPP3007U+x5UJA75ERHVMamoqtm3bJl1ARFHE33//jdRiX4qIiIjqA17XiOoXgyjiel4eXjt/HgDwZFiYRXnMxYuYduYMjmZlIV+vR75ej0MZGXj46NESQcJ3r1zBi2fP4mR2NgoMBqgNBlzOy8MXN26g3Z49OGE2evhKbi667d+PXxMSkK7VotBgwNmcHEw9dQqfGXNjFvfNzZuYduYM4vLzoTYYcDAjA0NiY7E9NRXDDh/GsawsaAwGXMrLw6STJ7G1WD7i9MJCdN+/H9/duoUkjQaFBgOu5OXh9QsXpP0vbktKCh45ehTncnKgMRhwLicHjx47hkN371b4WJu7W1iIIbGxOJCRAY3BgNtqNV47fx7vXblSpXbNVfV4mbx18SLeuXQJiRoNCgwGbElNxYjDh/GfhAQ8duwYLubmQmMw4GR2NkYeOYLzOTnSuqbzaU18vEWbO9PScFerRUdPT7R0d7fbPlPDdeDAAfj7+0uPtWvXAgA++OADi+W3zT63MjMzcfPmTSQlJZVoLyIiAitWrIBMJsPs2bPRuHFjdOrUCc2bN8elS5fQuXNnLFmypMb2rzwOFfD94osvIAgCpk+fXqIsLi4OY8eOhZ+fH5RKJaKiojB//nzodLoSdX/77Td06dIFLi4ucHd3R9++fbF9+/aa2AUiauBEUcSvv/5a6nLOat6w8LpGRPUdr2tEdV+eXg9h40bpIf/zTzTbuRP7797FzIgIvHfPPRb157VsiX+1a4ct3bohacgQ3Bo0CE+EhgIA3r50yaLuNzdvAgDmREUhfvBgJA4ejC3duuGRRo0wPiQE0V5eUt03L15EplaLaE9PHO7dG2lDh+LnTp2gksnw1sWLyLXyHedKXh42du2K9KFDsbVbN/gpFMjQajE4NhavRUYiacgQnOrXD129vQEAi65etVh/0dWruFVQgAhXV+zo0QPpQ4fiL2M7n16/juvGUavmruXnY1XHjkgbOhRH+/RBB09PiAA+MGt73b334rGQEADAwpYtUTBiRImR0sV9ffMmsnQ6+CkU+KtbN6QMGYLfOne2aWSwrap6vMzb2dOzJ9KGDsVa42t0JS8Pjxw7huVt2yJ1yBAc7NULUW5u0IsilpiNVJ4QFgYZgN8TE6HW66XlvxlHiI8znktUObU9sVpdmrRNq9UiPT1demg0GgBAfn6+xXK92XlYnokTJ2Lfvn144IEHUFBQgPPnzyMyMhLz5s3D/v374ebmVomeVg+HCfjq9XosW7YMMpkML7/8skVZQUEBBg0ahHXr1kGtViMoKAhxcXGYN28eYmJiLOpu3boVjz76KI4ePQovLy8olUrs27cPw4cP5wQTRFTtkpOTceHCBRgMBovlBoMBFy5cQHKxW6LIcfG6RkSOgNc1ovrLTS6Hj0IBJ0GwWK6UyTAtPBxDAwPh7eSEAKUSy9u2hZMg4FZBAdKMQRUASDb+/UZUFEKcnRHs7IyhgYFYd++9WBUdLdXTiyL+Mo4m/a5jR7T39ISbXI6HgoMxISwMuXo9dqallejjxLAwPBAUBF+lEkMCAzEjMhIA4OXkhHfvuQdBKhXae3riC2M+4lPZ2RbrbzSO4lvaujV6+vjAVS5Hfz8/zIyMhAHAn1Y+o/r6+mJi48bwUyrR2dsbn7RpU6JthUwGZ2NKDFNu5PKcMY6CfSkyEsMCAxGgUmFMSAget2MAtKrHy+SdFi3Q188PfkolHgsNlfrYzsMD0yMi4K9SoYevLz4w/lhwymwkd5iLCwb4+yNbp8NG4/HViyL+a3wtTIFyoqrq378/RFEs9xEeHi6tM2/ePIiiiN1W0qWY9OzZExs3bkR6ejrUajUuXryIuXPnwtnZufp3qgIcJuD7+++/4/r163jwwQcRFRVlUbZmzRpcv34d/fv3R0pKCm7fvo0jR45ApVJh2bJlyM/Pl+ouXLgQAPD5558jKSkJ6enpmDVrFnQ6HRYtWlSj+0REDU9QUBBatWoFmczy41kmk6FVq1YICgqqpZ5RTeN1jYgcAa9rRHWfkyCgYMQI6ZE3fDhO9+uHNh4emHf5Mt4pNmr3Yk4O7j98GL5btsB582a4bN4M361boTOO2M8zGy3X3Djarc/Bg3jl3DksvnoVK2/dwgWzW/wBIFWjkdbrtHcvXIztumzejO+MOXyvWBlt287T0+J5S+P22nt6QjALVLcypgjI0GpRYNa/GwUFAICHjx612KZpn23ZZisPDwDAHWNblZWkVgMAWhRLZ2A+Crqqqnq8pHaM+yy1Y6zfsVhfTccm3rhvJhNNaR2MeY73pKcjtbAQ3X18EO7qWrGdIiKrHCbg+9FHHwEAXn311RJlZ8+eBQBMmTJFGl7dqVMn9OzZE1qtFpcvX7aoq1AoMG3aNABFM/G98MILAIBz585V6z4QEQmCgLFjx5a6XCg2woIcF69rROQIeF0jqvtUMpk0CtVZLoerkxPaeXpiTadOEAAsu34dGmPQT63X4/5//sHmlBRklJJqwDxRyxft2iFYpcLp7Gx8fP06Xr9wAU+dOoXWu3fjoX/+gd4YJFYXuwvAGmspHYqTGz9TZMU+W+Rmz/VmqWQ05Ww314ZbvU1jd8vfg7KZ1q/JIE1Fj1e57RRfXkobYxo1gptcjr9SU5FRWIjfjJPejefoXiK7cartDtjDnj178M8//6B79+7o1atXifJM46yTnsV+zTI9zzSbvTIrKwteXl6Qm91yYa0eEVF1CQgIwJAhQ7B161aIoghBEDB48GAEBATUdteohvC6RkSOhNc1ovopUKVCgFKJlMJCXM/PRysPD/yTmYnr+fkIc3bG9h49LCbXCt++HTeLjXLt7++PO4MH40RWFm7k5yNDq8WVvDx8dfMm/khOxh9JSXi4USMojXcBBCqVSB46tMb2USEI0Ioi8keMgIsNaReqkylYWjxwbLAScDUFWOtrHnQ3JyeMDg7G6vh4/JKQgPVJSZABeJQB3ypzmFGdVGUOEfA1zYL32muvAQBiY2OhVqvRu3dvODnV3C5qNBopCTQAZBvz3QiCwNEL1cx0fHmcyVEMHToUsbGxyMzMhJeXF4YOHcrzuxrVtWNbF65rvKbVLl7XyNHwulazeGzJHpLUaqQWFgKAlIM2yfjdoLuPj0WwtzS5Oh3cnZxwr7c37jVOBAYAWoMBn8TF4VJuLgDAX6mESiZDSmEh4vLzEWF2W7/OYICTrHrCWGEuLojLz0dsRgYG+PtLyw2iCAE1+15qZMz/ecV4TEyOmeW/NQk11o1Xq2EQRWmEriiKSCiWPqGumti4MVbHx+M545wSA/z8pGNARFVX7wO+58+fx+bNmxEZGYnRo0cDAMaNG4ebN28iIyMD3mYXler2wQcfYP78+SWWR0RElBiFRdUjIiKitrtAZDczZ87Ev/71Lzz33HO4p9jsyGRf2aVMSFEb6sp1jde0uoHXNXIkvK7VnLp0XaO6T2MwQG2WuiBPr8e5nBy8fuECRAARrq5SANb07/a0NGxNSUF3Hx/oRBH7794tMbo3U6tF13370MHTE69HRUkB38u5udhknKAtSKUCUDQR3OCAAPyZnIyxR4/is7Zt0crDA2ezszH97FlMadwYLxknGLOn+wMD8fmNG3j29Gl82a4d7vXyws2CArx87hw6eHrik7ZtK922u/FH+iu5uVDr9eVO3NbB0xM/xcfjk7g4dPH2RlcfH+xKS8OvxnQH5pq4uMBHoUCGVouFly/jnRYtAACLrl6Ftp6M+h3o749QZ2cpv+84O05OR0QOEPD96KOPIIoiXn755RKTQdS0OXPm4JVXXpGeZ2dno3HjxoiLi4NHsaTmZF+CICAiIgJxcXH19rYWouKCgoLw/fffIy4uDtevX6/t7ji0nGITh9SmunJd4zWtdvG6Ro6I17WaU5eua1T36UQRLps3Wy2TCwI+Mwt6dvLyQmcvLxzLysKww4fLbPdoZibi1WpcycvDb4mJcJXLIRcE5Bhz8YY4O+PhRo2k+otbtcK+9HQczcpCzwMHLNr6+Pp1TG3SBB52vtMppkUL/JGUhKt5eRgSG2tRdio7Gy9HRqJpJScRM0229s2tW/jm1i1kDBsGb4Wi1Pr/17QpPrx6FWmFhRhqdmybuLjgVrFgupNMhtnNmmHOxYuYd/kyPrp2DYIgoNCGXMh1hUwQ8HhoKJZcuwagKK8vEdlPvQ74JiQkYM2aNfD19cWUKVNquztQqVRQGX+hNCeKIv+zVkN4rMkR8byufnXl+Nal6xqvaXUDjzc5Ip7X1Y/Hl6rKXS5HNx8fvN28OfqZpTqQCwI2du2KuZcuYXtaGhLVaoQ6O+PVZs3w4dWrFqN8BwUE4Hz//vg0Lg5bU1Jwo6AAar0eTV1cMCggAO80b24RAG3l4YEjffpg/uXL2J6WhruFhWjs4oKxISGY1ayZ3YO9QNEI43+M29yUnIwkjQZBKhVGBQdjTlQUwlxcKt32hNBQ7ElPx4akJGTZMOGcl0KB7T164NVz5xCbkQEPJyc8Hx4ONycnzDp/vkT92VFRKBRFrLh5E6mFhWjt5oaP2rTBUydPlhhtXVc1N04+PCIwEH5KZS33xjEwhy+Z1OuA76efforCwkJMmzZNmqWciIiovuJ1jYiIiGpKuKsrxJEjK7xeI2dnfNOhQ4nlz4WHl1jW1NUVy9q0Adq0sant5u7uWN2pU7n1VkVHY1V0dInlj4SEQLQy8ZezXF7qvgY7O+Nf7duXu815LVtiXsuWJZb7q1RW21bJ5fjBSh/LEu3lhZ09e5ZY/lqzZiWWyQQB77RoIaVzMLkxaFCJuvY6Xrut9M3UP2t9vMfDo8xz7IIxX/E4TtZGZHf1Ovi/bds2AMD7778vTSIjCAJu3rwJAPDx8UFmZqaU77B4LivTcx8fH2mZl5cX8vPzoTfLYWStHhERkb3xukZEREREjk6t12NnWhq+u3ULzjIZHmI6ByK7q9cB39atW6Nz584lHibNmjWDk5MT2hh/TVy5ciXy8vIAAMePH8fBgwehUCjQvHlzaZ02bdpAq9Xiq6++AlB0O9Tnn38ulREREVUXXteIiIiIyNFNO30aAw8dQrZOhwlhYdWSroOooRNEB0zwFB4ebjGbeUFBAVq3bo0bN27A1dUVfn5+iI+Ph8FgwOuvv45FixZJ6/71118YMWIEgKKJJbRaLe7evQsnJyccP34c7dq1s7kf2dnZ8PLywokTJzjBTTUTBAGRkZG4fv06c5aRw+B5XXNycnIQHR2NrKwseHp61nZ3SqgL1zVe02oW3//kiHhe15y6fl2rM0aNqu0eEDVIU06exB9JSRgeGIh/tWsHzzIms2swNmywSzMfCoJd2qmLXud3hwqp1yN8beXi4oK///4bDz/8MFQqFRITExEeHo558+bh3Xfftag7fPhw/Pzzz4iOjkZmZibUajV69+6Nv/76q0LBXiIiourC6xoRERER1VcrO3bE3WHDsKZTJwZ77UzmwA+qGIccN3/jxo0Sy6KiovCf//zHpvXHjRuHcePG2blXRERElcPrGhEREREREdmKQXIiIiIiIiIiIiIiB+GQI3yJiIiIiIiIiOypQK/HgsuXsS4hAbcKCuAql6OHjw/mtWyJbj4+5a6fUViIuZcv48/kZCRrNIh0dcUToaF4pVkzKGX/G493Iz8fETt2lNrOv9q1w7TwcHvsEhE5KAZ8iYiIiIiIiIjKMfrIEWxNTZWeZ+l02JKaip3p6TjYqxc6e3uXum6BXo8Bhw7hVHa2tOxsTg7mXLyIfzIz8XuXLtXZdWogeBs/mTDgS0RERERERERUhgN372JraiqcBAHfd+yIoYGBSNJo8MypUziUkYH3rlwpM2j7WVwcTmVno5mrK9Z06oTmbm7YkJyMqSdPYn1SEi7l5qKlu7vFOm5yOdKGDi3RlkLGsB4RlY2fEkRERERERETkEG7k50PYuBEDDx1CemEhXjp7FuHbt8Ppzz8x6/z5Srd7OCMDADAiMBCPh4XBT6lEGw8PLGrVqqg8M7PM9ZM1Ggz098eX7dqhm48PfJVKTG7cGNFeXgCAS7m5VtdzlstLPOSCUOn9IKKGgSN8iYiIiIiIiMghnMzKAgA4y2Rov2cPDKKIPr6+aKPXo1sZKRfKk6PTAQAau7hYLG9ifJ6t1Za5/rI2bawu14siAMBHoShRphNFPHn8ODYkJ0MhCBgaGIglrVsjxNm5wv0nooaFAV8iIiIiIiIicggnjTlyN6ek4LmmTfFxmzZQyeVlrqM1GKTAa3FKmQwyQYD10v8pr7z49gr0eqxPSsKp7Gy0dHNDDyuTvmkMBqyOj5ee/xQfj38yM3Gib1+4OzGcQyXxNn4y4ScEERERERERETkE06RofXx98Xm7dpDZkP7gmVOn8P2dO1bLdvXogf7+/nbto/n2Bvn749sOHeBUSl7eN6Oi8FJkJE5nZ2Pc8eO4mpeHb2/dwkuRkXbtExE5Fgb/iYiIiIiIiMghmFI6fNS6tU3B3tp2q6AA1/PzLZYFqVTY2LUrdvTogfdatUKASoWBAQF41Rjk3Z6aWhtdJaJ6hCN8iYiIiIiIiKjey9JqcaOgAGHOzuhSgXy9Kzp0wFft21stU5Yy8rYqVkVH4+v27fFncjLGHz+OB/75B9cHDkSQSgUAcJHL8UBQUIn1Onh6AgAS1Gq794mIHAsDvkRERERERERU75nSOfT184NQgdG9NZ3SAQBUcjnGhITgkaQk/Bwfj1/i4zGjnDQNLsZcxIWl5Bsm4m38ZMJzgYiIiIiIiIjqPVM6h47GkbD2VF74uLLJI5q5ugIAbhQUlFs3X68HACjrQaoKIqpdHOFLRERERERERPXeSeMI32gvrwqttyo6Gquio8us4+5UFD65XSwwe8v43MOp9PBKocGAqB07UCiKiO3dG+HGIK95ey5mqSM2JSdj0okT6OXriz+6dpWWm/Yv1MXFlt0iogaMI3yJiIiIiIiIqN4zpXSojhG+3X18AACbU1Lw0507SC8sxLmcHLxx4QIAoJux3KTQYIBar4deFKGUydDawwPJGg2eOnkSZ7OzcbewED/Hx+PnhIQS6/f29UWeXo8Nycn4Ii4O6YWF2J6aimXXrgEABldDigkiciwc4UtERERERERE9ZrOYMC5nByEOjvD3zj5mT318vXF0IAAbE1NxRMnTliUKWUyxDRvbrFsSGws9qSnY2XHjpjcuDGWtG6NgwcOYFd6Otrt2WNRt7uPD+43m6TNS6HA4tatMePsWbxgfJi0cnfH002a2H3/iMixcIQvEREREREREdVrF3NzoTEYqmV0r8n6Ll3welQUIl1doRAEeDo5YWhAAPb07InO3t5lrtvO0xNH+/TBYyEhCFAqoRAENHVxwSuRkdjWvTvkxfLyvhgRgW/at0crd3coBAEBSiWmNG6M3T17wrWM9BHUsMkc+EEVw08JIiIiIiIiIqrX2np6Qhw5slq34SKXY1GrVljUqlW5dXf37FliWQt3d6zt3Nnm7T3TtCmeadq0Qn0kIgIYJCciIiIiIiIiIiJyGAz4EhERERERERERETkIpnQgIiIiIiIiIiKq54Tyq1ADwRG+RERERERERERERA6CAV8iIiIiIiIiIiIiB8GALxEREREREREREZGDYA5fIiIiIiIiImpQ5l26hPmXL2N6eDg+b9eutrtjkwK9HgsuX8a6hATcKiiAq1yOHj4+mNeyJbr5+JS7vl4UsezaNay8fRvX8/OhEAREe3nhzebNMSwwsNT1rubl4Z5duyADcOW++9DU1dWOe0X2JK/tDlCdwRG+RERERERERER13OgjR7Do6lVcy8+HVhSRpdNhS2oq+h48iGOZmeWuP/3MGcy+cAEXcnOhMRiQq9dj3927GHH4MDYmJZW63sLLl6EXRTzdpAmDvUT1BAO+RERERERERER12IG7d7E1NRVOgoA10dFIGzoUZ/v3Rw8fHxQaDHjvypUy179dUIAVN28CAJa1bo2UIUNw7b77MCooCCKAuZcuWV3vSm4u1sTHw1kmw1vNm9t7t4iomjDgS0RERERERERUhx3OyAAAjAgMxONhYfBTKtHGwwOLWrUqKi9nhO+RzEwYALT39MTLzZohQKVCpJsblrdtCwA4mZ0NjV5fYr0FxtG905o2RaiLi133iYiqDwO+RERERERERFRlq27fhrBxIx44fBjbUlIQvWcPnDdtwj07d2L1nTsVbk9nMCBo61YIGzfiH2PA00RrMMBvyxYIGzfiiFmwc0dqKobFxsJ3yxaoNm1Cq1278OHVq9CLok3bnHfpEoSNG/HCmTMWy2/k50PYuBHumzeXWOdybi4eO3YM/sZtttu9G/++davC+1uWHJ0OANC4WNC1ifF5tlZr2/rOzpbrG1M0iAByiwV8L+Xm4ueEBLjK5ZjD0b31gsyBH1QxnLSNiIiIiIiIiOzmSGYm7v/nH+iMQdZLeXl48sQJhDo7Y4C/v83tOMlkeCw0FMvj4vBrQgK6mk1MtiMtDXe1WrRwc0MXb28AwNaUFAw7fNiijYu5uXjjwgVcys3Fvzt2rPK+FXclNxfd9u9HplnA9WxODqaeOoVcnQ4zIiPLXN8giig0GKyWyQUBCllRqKu8cHVVywFALBYUN43ufTEiAj4KhQ0tEFFdwSA5EREREREREdlNSmEh3r3nHsQPHoyTffuis5cXAGDx1asVbuvJsDAAwLrERIuA5K8JCRblADA0MBB/dOmCHzp2xM2BA5EyZAg+btMGQNHo4+t5eZXep9K8efEiMrVaRHt64nDv3kgbOhQ/d+oElUyGty5eRK5xZG1p9qanw2XzZquPZ06dsnt/bXU9Lw9r4+MBAD/cvg3lpk0I2bYNy65dq7U+EZHtGPAlIiIiIiIiIrsJc3bG61FRCHF2RgcvLynoeiYnp8JtdfH2Rks3N9wqKJDy1GoNBvyRlAQBwBNmAV8AGBUcjCcbN0YjZ2d4ODlhWtOm6OHjAxHA8aysKu6ZJb0o4q+UFADAdx07or2nJ9zkcjwUHIwJYWHI1euxMy3NrtusKT/cuQPTuONEjUb699Xz57GmEuk5iKhmMaUDEREREREREdlNB09Pi+ct3N0BAIlqtcVytZVJwgBAAKCSy6XnT4SF4Z1Ll/BrQgK6+/hI6Rx6+fggwpiDFgAytVq8eeEC/puUJAUpzeWVsr3KStVopDY77d1rtc6VckYV9/XzQ8GIEVbL5IJQtQ5WwS/GEdSjg4Pxdfv2yNPr8cypU9ieloZFV6+WCLQTUd3CgC8RERERERERVRtT6LZ4ploXKxOgAUBTFxfcGDRIej4hLAxzL13CbwkJWNq6tdV0DgDw/Jkz+NmYhsAa26Zts526lNy75mxJ6TDg0CGrZZPCwrAqOrpSfauKPJ0Ol3JzAQAft2mDAJUKAQD+1b49mu/ciQu5udAaDFJ+Yao7+IqQCQO+RERERERERGQ3xQOrprBoZcerRri6oqePDw5kZGDf3bv4IykJSpkMY0NCpDp6UcRvCQmQAfiza1cMCQyURshOPnEC39uYhsC0ji3BYaUx4BmoVCJ56NAK7VNFlXfsqloOAIJx3zO0WogAnAQBTc1GUDdzdYWTIEAnikgtLESIs7MNrRJRbWDAl4iIiIiIiIjs5lR2tsVzU1qD4gFCceRIm9ucEBaGAxkZePHsWdzVavFQcDB8lEqpPL2wEFpRhL9SieFBQZXue6ixj7cKCiyWxxdLRwEA/kolVDIZUgoLEZefb5FeQmcwwMmGEbD9/f1tOg7uTkXhm9vF+mXqp4dT2eEdd2OKjNvF9uNWfj6AooCwqY63QgEBgE4UkahWo5HxmCRpNNCJImQAfBSKcvtMRLWHo72JiIiIiIiIyG7i1Wq8d/kyEtVqnMnOxsvnzgEA2nl4VLrNsSEhUMpkOG0MJhdP5xCgVMJNLkdaYSE+u34dqRoNsrVaHMnMxIbk5BLtmQKocfn5KDRLzdDRmH94S0oK/k5NBQDk63SYdf58iTaUMhkGBwQU9e/oURy6exeZWi32p6ej8759+OT69Urvb3HdfXwAAJtTUvDTnTtILyzEuZwcvHHhAgCgm7EcAAyiCLVeD41ZzuKuPj6QATidnY2Pr11DqkaD63l5mHH2LICivMumvMnuTk6I9vICUJQm42Z+Pm7m5+P5M2ektlzMciwTUd3DEb5EREREREREZDchzs5459IlxFy6ZLH8zebNK92mr1KJEYGB+G9SErwVCtwfGGhRLggCnm7SBJ/GxWHmuXOYaQwyl6artzeAogCqatMmnOjbFx29vNDJ2xtDAwKwNTUVQ2Jj4atQIFevLzUlwuJWrbAvPR1Hs7LQ88ABi7KPr1/H1CZNyh19a4tevr5Sv544ccKiTCmTIcbs2JryApvnQm7s4oKnmzTBN7du4ZXz5/GKWQBbADC/ZUuLNt9t2RIP/PMP/puUhP8mJUnL5YKA9+65p8r7Q9WDozrJhOcCEREREREREdlNtKcnfu3cGS3c3KCUydDGwwO/dO6MPn5+VWrXNKr30UaNpNGo5ha1aoX37rkHrdzd4SqXI1ilwgvh4RhnluvXpK+fH95p0QL+ZmkhTH7q1AkTw8Lgo1BAYzBgoL8/tnTrZrVPrTw8cKRPHzwRGooglQoKQUCkqyveiIrCib597RLsNVnfpQtej4pCpKsrFIIATycnDA0IwJ6ePdHZGMAuy5ft22NRq1Zo5e4OpUwGN7kcfXx9salrV4wKDraoOzwoCJu7dUMvHx+4yGRwNdbd1r077vP3t9s+EVH1EERRtPdElWSUnZ0NLy8vnDhxAh5VuHWFyicIAiIjI3H9+nXwlCZHwfO65uTk5CA6OhpZWVnwNN7GR5Z4TatZfP+TI+J5XXN4XbPRqFG13QOHs+r2bUw5eRL3Bwbiz1ICpERkxYYNdmnmO6GyUyPWfVP53aFCOMKXiIiIiIiIiIiIyEEwhy8REREREREREVE9x1GdZMJzgYiIiIiIiIiIiMhBcIQvEREREREREVXZ5MaNMblx49ruBhFRg8cRvkREREREREREREQOggFfIiIiIiIiIiIiIgfBlA5ERERERERERET1HEd1kgnPBSIiIiIiIiIiIiIHwYAvERERERERERERkYNgwJeIiIiIiIiIiIjIQTCHLxERERERERERUT3HUZ1kwnOBiIiIiIiIiIiIyEEw4EtERERERERERETkIBjwJSIiIiIiIiIiInIQzOFLRERERERERERUzwm13QGqMzjCl4iIiIiIiIiIiMhBMOBLRERERERERERE5CAY8CUiIiIiIiIiIiJyEMzhS0REREREREREVM/Ja7sDVGdUOOAbHx+P7777rjr6gnfeeadS6124cAGzZ8/GoUOHIJfLce+99+LDDz9E27ZtpTr5+fmIiYnBb7/9hqSkJPj6+mL48OFYvHgxAgICLNo7efIk3nzzTRw4cABqtRpRUVF4/vnnMX369CrtHxERkS14XSMiIiIiIqLKqnDA9/jx45g3bx4Ewf5z/1Um4Hvz5k307NkTmZmZ8Pf3h1arxebNm3HgwAGcP38eISEhAICpU6di7dq1cHJyQkhICJKSkrBq1SpcvHgRBw8elPYnOTkZAwYMQGZmJjw8PODv74/z58/jhRdegFwux7Rp0+y6z0REROZ4XSMiIiIiIqKqqHDANycnR/p74sSJVe5AbGwsLl26VOkA8oIFC5CZmYmXX34ZH330EQwGA6ZMmYLVq1djxYoVmDt3Lq5cuYK1a9ciICAA//zzD8LDw5Gamop+/fohNjYW27dvx+DBgwEAy5cvR2ZmJsaPH4/vv/8eCoUCf/75J0aOHImFCxfyP8ZERFSteF0jIiIiIiKiqqhwwLdZs2bS3ytXrqxyBw4fPowePXpUen0vLy888cQTeOeddyCTySCTyTBhwgSsXr0aly5dAgCcO3cOAPDAAw8gPDwcABAQEIBx48Zh7ty5OHfunPQf47NnzwIApk2bBoVCIa0XERGBuLg4ZGZmwtvbu9L9JSIiKguva0RERERERFQVFQ74+vr6YtSoUXZL6VDV9pYtW1ZiWUFBAQDAw8MDAJCZmQkA8PT0tKhnem4qt7Uu/2NMRETVhdc1IiIiIiKqDFltd4DqjAoHfJs3b47//ve/duuAvdtLTk7GJ598AplMZpeUE0RERLWJ1zUiIiIiIiKqiAoFfDUaDfbu3QuFQoG+fftCJpMhNzcX33zzDa5evYqoqCg89dRTtTZSyNvbG1lZWQgMDMSvv/6KXr161ej2NRoNNBqN9Dw7OxsAIAhCtUxyR/9jOr48zuRIeF7XnLp6jGvzusZrWu3i+58cEc/rmsNjTERE1LDZHPBNSEhAv379cP36dYiiiM6dO2Pz5s0YNGgQzp8/j+DgYCQkJGD58uU4cuQI/P39q7PfVrVo0QJxcXFITU3FDz/8gCFDhki3v9aEDz74APPnzy+xPCIiosSttFQ9IiIiarsLRHbH87r6mYKZdU1tXtd4Tasb+P4nR8TzuvrV1esaERER1QybA77z5s1DYWEhtmzZAl9fX8yePRsPPPAAtFotbty4gdDQUFy7dg2DBg3C4sWLsXjx4urst1X//PMPAODtt9/Gu+++i7feegufffZZjW1/zpw5eOWVV6Tn2dnZaNy4MeLi4mo08NwQCYIgTUAkimJtd4fILnhe15ycnJza7oJVtXld4zWtdvH9T46I53XNqavXNSIiql7M4UsmNgd8t23bhrlz50qzfi9fvhxt27bF999/j9DQUABAs2bN8Oqrr+Jf//pXrQR8TV577TW8++67+Ouvv2p0uyqVCiqVqsRyURT5pbaG8FiTI+J5Xf3q+vGtjesar2l1A483OSKe19WPx5eIiKhhszn4n5SUhObNm0vPo6KiAAAtW7a0qNemTRvcunXLTt0rW2ZmJt599118+eWXFsudnJykcgBSTuHitzaZnvv4+EjLyqvLmcyJiKi68LpGREREREREVWVzwNfLywvp6enScycnJ7Rs2bLEbZ25ubnSf0yrm4eHBz799FO88MIL2LBhA4CiX7M/+eQTAJAC1G3atAEA/Pnnn7hx4wYAIC0tDT///LNFufnfX331FXQ6nbReXFwcQkND+R9jIiKqNryuERERERERUVXZHPBt3bo1YmNj/7eiTIYLFy6gVatWFvVOnz6NZs2a2a+HZZDL5Xj//fchiiIefPBBNGrUCAEBAYiJiQEAzJo1C0DRf5DHjh2L1NRUNG/eHE2bNkVoaCguXryI7t27Y+DAgVKbL774Ijw9PfHzzz/D19cXoaGhGDlyJABI7RIREVUHXteIiIiIiKiyZA78oIqx+ZjNmTMHHTt2LLfesWPH8Nhjj1WlTxXyzDPPYN26dejevTuys7MhiiL69u2Lbdu2YfTo0VK97777DjNmzEBQUBASEhLg7e2NSZMm4Y8//oAgCFK94OBg7Ny5E0OGDAEApKamolWrVli+fDmmTZtWY/tFREQNE69rREREREREVBWCyIz+1SY7OxteXl44ceIEZzSvZoIgIDIyEtevX+ckFeQweF7XnJycHERHRyMrKwuenp613Z06ide0msX3Pzkintc1h9c1G40aVds9ICIqYkznVlX/MRv44WjG8LtDhXBUNBEREREREREREZGDqJbZ1RISErB3714kJSWhsLCw1HqzZ8+ujs0TERERERERle3kydruARGRXXFUJ5nYNeCr1+sxc+ZMfP311zAYDOXWZ8CXiIiIiIiIiIiIyH7sGvCdN28evvzySwCAi4sLWrVqxTx/RERERERERERERDXErgHfH374AQDQuXNnbNq0CYGBgfZsnoiIiIiIiIiIiIjKYNf0HmlpaRAEAW+++SaDvUREREREREREREQ1zK4B365duwIoSudARERERERERERENUPmwA+qGLses2XLlsHd3R2ffvopCgoK7Nk0EREREREREREREZXDrjl8o6OjsWfPHnTq1Anu7u7o0qULfH19rdYVBAGbNm2y5+aJiIiIiIiIiIiIGjS7BnwTExMxYcIECIIAURTxzz//lFpXEAR7bpqIiIiIiIiIiIiowbNrwPell17C+fPnARTl8+3Tpw/c3d3tuQkiIiIiIiIiIiIqhrluycSuAd+tW7dCEARMnjwZ3333nT2bJiIiIiIiIiIiIqJy2DX4L5fLAQDjx4+3Z7NEREREREREREREZAO7BnwffPBBAEBaWpo9myUiIiIiIiIiIiIiG9g14PvJJ5+ge/fumDdvHk6fPm3PpomIiIiIiIiIiKgUggM/qGLsmsM3ODgYBoMBWq0W0dHREAQBSqXSal1BEJCXl2fPzRMRERERERERERE1aHYN+KrVaovnoiiWWGYiCIzPExEREREREREREdmTXQO+u3btsmdzRERERERERERERFQBFQ743r59G4GBgVCpVCXK+vXrZ5dOEREREREREREREVHFVXjStn//+98IDAzExIkT8eeff0Kr1VZHv4iIiIiIiIiIiMhGcgd+UMVUOODr4+ODnJwcrF69Gg8++CCCgoIwdepUbN26FXq9vjr6SEREREREREREREQ2qHDAd8aMGYiLi8MHH3yADh06IDMzEytXrsSIESMQHByM5557Drt27YIoitXRXyIiIiIiIiIiIiIqRYUDvgDQtGlTvP766zh+/DguX76MBQsWoFWrVkhPT8fXX3+NQYMGISQkBDNmzMD+/fvt3WciIiIiIiIiIiIisqJSAV9zUVFRiImJwdmzZ3Hu3DnExMSgefPmSE5Oxueff45+/fqhcePGePXVV3H48GF79JmIiIiIiIiIiIjMyBz4QRVj12PWqlUrLFiwABcvXsTx48cxe/ZshIeHIz4+Hh9//DF69uyJiIgIvPHGGzh+/Lg9N01ERERERERERETU4FVbkLxjx45YtGgRrl27hsOHD+Oll15CaGgobt68iSVLlqBLly5o3rw53n77bZw5c6a6ukFERERERERERETUYNTIqOguXbpg2bJluHXrFvbt24fnn38eQUFBuHbtGt577z1ER0fXRDeIiIiIiIiIiIiIHFqNp8Ho1asXli9fjvj4eOzYsQPPPPMMfH19a7obREREREREREREDqO28+wyh2/dUWvHTBAEDBgwAF9//TUSExNrqxtEREREREREREREDsPJno39+uuvVW5j7NixdugJERERERERERERUcNj14DvuHHjIAhCpdcXBIEBXyIiIiIiIiIiIqJKsmvAt0mTJsjIyEBOTg4AICQkBAqFAgCgVquRnJwMAAgMDISLi4s9N01ERERERERERNRgMdctmdj1XNi3bx+8vLzQsWNHJCQk4M6dO4iLi0NcXBwSExNx7do1tGrVCu7u7tixY4dUZv4gIiIiIiIiIiIiqqrNmzdj0KBB8PX1hZubGzp16oTly5fDYDBUqB1BEGx6fP/99xbrrVq1qtx1tmzZYs9dBmDnEb4zZ85EfHw8vvzySwQHB5coj4iIwPvvv4/Ro0fj+eefr5YdIiIiIiIiIiKyl3/l5qKnUokOSmVtd4XsaLtajUyDAY+4utZ2V6iaLFq0CHPmzAEAREZGwt3dHadOncKMGTOwfft2rF+/HjKZbWNhe/XqVWpZRkYGzp8/DwDo3r271TqBgYFo3ry51TIfHx+b+lARdg347tixo6hRp9KbValUAIADBw7Yc9NERERERERERHZ1QKPBjIwMeMtkuNmoEVxtDA5R3XZHp8MDqalQCgI6KhSIMqYjJcdx6NAhvPnmm5DJZFi9ejXGjx8PADh16hSGDh2KDRs2YNmyZXjttddsam///v2llsXExOD8+fPo2rUrWrZsabXO8OHDsWrVqgrvR2XZ9ZNKafy1a8WKFVaHRouiiO+++w4A4MpfUIiIiIiIiKgBuqHTQbh9G+537pRa54HUVAi3b2NVXl4N9ozMFYoinkhPhw7AKx4ecJXJIIoi/pWbC487dyDcvo2PsrMt1tGJIt7NykLzxESobt9Go/h4PH33LlL1+hLtG0QRK3Nz0SUpCV537iA4Ph5DUlKwX6Ox2p+bOh2eTE9HcHw8FLdvIyQ+HlPS05Fgpe2K+Cg7G8Lt25icnl6lduqTMCcnTHRzQ44oYuLdu7XdHaoG7777LkRRxNNPPy0FewGgQ4cOWLZsGYCiEcBarbZK2xFFEWvWrAEAPPnkk1Vqy57sOsJ35MiRWLVqFf773//innvuQc+ePaXAbkFBAWJjY3H58mUIgoDRo0fbc9NUQ0RRRGFhYW13w4IoitBqtcjMzIRara7t7lhQKpUQBKG2u0FEREQNCL+vVQy/rxFRab7Py8NNvR73KhR43cMDADA1IwMrywjCP3P3Llbl50vPkwwGfJeXh0MaDY4GBcHFbITwsxkZ+M6srWxRxN8aDf5OScEaX1887uYmlaXr9eiVkoJ4s+BuosGAVfn52KPR4GRwMDw5+rhCPvH2xma1GocKC7FdrcYgZ+fa7lKV8Qwokp2dje3btwMApk6dWqL80UcfxXPPPYf09HTs2rULQ4YMqfS29u3bhxs3bkChUGDcuHGVbsfe7BrwXbp0KS5evIjY2FhcvXoVV69elb48iaIo1evduzcWL15sz01TDSksLMSrr75a292oN5YuXSqlMSEiIiKqCfy+VjH8vkZE1oiiiMU5OQCAOZ6ekBljG9d1OnRQKOAiCIgt9uPaJa1WCvZ+5eODMS4uOKXVYlx6Os7rdPgxPx/PursDAK7pdFKwd76nJ6a5uyPbYMCrmZnYoFZjTlaWRcD3q7w8xOv1aCKXY42fH9ooFDheWIjH09MRp9djVV4eZhiD0mQbV5kML7u747WsLCzKznaIgC8VOXHiBAoLC+Hs7IxOnTqVKFcoFOjSpQt27NiBw4cPVyngu3r1agDAsGHD4O/vX2q9U6dO4fHHH0dSUhI8PT0RHR2NCRMmoFmzZpXedlnsGvD18fHBgQMHsG3bNuzduxcpKSnQ6XRFG3JyQlBQEPr27YvBgwfzV3QiIiIiIiIiqpPOarW4qtOhsVyOh1xcpOVvenpioEqFqXfvlgj47jamYrhPpcL/GQO7A+VyzHB3xzvZ2dit0UgB33PG28hbOznhHS8vAECgXI4Vvr7YkJCAW3o9sg0GadTuYWPbL3l4oLfxR6qBzs6Y7u6OudnZOFzH7uyoL551d8dbWVnYrdEgy2CAF0dJO4QrV64AAJo0aVLqPGORkZHYsWOHVLcyNBoN1q1bB6D8dA4nT57EyZMnped//PEHFi5ciPnz5+Ott96qdB9KY9eALwAIgoChQ4di6NCh9m6a6gClUomlS5fWdjcsaDQavPnmmwCADz74QMolXRfUpb4QERFRw8DvaxVTl/pCZKuf8/LwcW4uzmq1kAFor1BglocHRhebK6d/Sgr2aDRY5+eHR8zKVuXlYcrdu7jf2Rl/BgRIy5P0erydlYUtajWS9Hq4CQLaKhQY6+qKp93cSkxY9lNeHj7OycE5nQ4KAH1VKnzo7Y3WNk6AdVWrxcLsbPytViPNYECgXI4HnJ3xnpcX/ORyi7pJej1mZmRgi1oNGYBHXV3xuKsrBqSmoqlcjhshIRb1j2g0mJedjQMaDTQA2ikUeNvTEyPNgrdl2WkMsA5UqaTRvQAwpIxRoOnGuYwiiwWYIozPzfP4epruhi7Whum5AoCr2XZzjHdNNy52XJoY2862Mo+SNf9oNHglMxNHCwsRKJdjpjEAXdxutRoDUlPRxskJZxs1sigLT0jATb0eR4KCcK/xM3Ryejq+z8/HEi8veMhk+DA7G4kGAzopFPjUxwf3KpXYpVZjVmYmzmq1CHdywpuenphoNooZ+N85+6ufHy5qtfhXbi4yRRH9VCos9/ZGlEKBdfn5eCcrC3E6HVopFHjfywvDzV7XNomJOK/T4e+AAItRuzpRRFBCAu4aDLgYHIyWCgU8ZDJ0V6mwR6PBHo0Go2w8P6jmaTQaaIrlt1apVFbv0snIyABQNDC1NKYyU93K2LhxIzIzM+Hl5YWRI0darePt7Y0XX3wR48aNQ1RUFLy8vHDhwgUsW7YMP/74I2JiYuDl5YUXXnih0v2wxu4BX3JsgiDU6VveVCoVv7QTERFRg8bva0SObVlODl7NzLRYdqiwEA+np+MbgwHPlBLAK49WFDEgJQUXjXfpAkCWKOJAYSEOFBZidX4+DgcGSnfrfpSdjVlZWVLdAgB/qtU4kJKC40FBCC9lVJ1Jsl6PrikpyDALVMbr9fg6Lw/7NBocCw6Gs1nA89G0NOw3G8W6Ii8P68xy5Zrbr9FgUEoKzENDRwoL8WBaGn7388NDNkwibxox268Ct/k3MgbEr5kdQwC4anxunmO3t0qFFk5OuKDT4YPsbDzv7o4cgwEzjK/tRDc3OJntf/HAcHHllQPAXb0eQ1JTkWUMHt/W6/FaVha87XgH9jd5ebhitv8HCwsxJDUVv/r54f7UVJhewUs6HSbdvYsgmQxDrQRZ38rKsmhni1qNEWlp+MDLC4+lp0v7e1Krxci0NJwODpZ+aHjSzQ1zsrKwJi/PIuC7U6PBXYMBHRUKtDT7UaKfMeAb6wABX0cen/zBBx9g/vz5Fsvmzp2LefPmlahrmi+grO8bpu9KBQUFle6TKZ3Do48+CudSPiseeughPPTQQxbLOnbsiB9++AF+fn745JNPEBMTg0mTJsHDjmlZqnQu5OfnY8mSJRg8eDA6deqERx55BDt27JDKL126hCeffBKNGzeGi4sLWrRogTfffBO5ublV7jgRERERERFRfVYoilCX8Sgux2DAO8Yg6zNubrjVqBHiGjXCBGMA842sLGisrGeLvRoNLup08JHJsDUgABmhobgQHIwVPj5o7eSE9728pGBvul6Pt7OzAQCveXjgZqNGuNmoESa5uiLDYMA8s0BwaYLkcuwJCMAn3t64EByMu6Gh2OTvDx+ZDOd1OvxsNpnZIY1GCvZ+5u2N5JAQHA4MRGYp+/pyRgY0AAarVDgTFISUkBB85u0NAHglM9NijqHSJBlH4zYtNqK2LKbRrrs0Gnydm4s0vR7b1Wp8boyBdDILPjkJAjb5+6OpXI43s7LgHR+PxomJWF9QgBHOzlJ/7enrvDxkiSL8ZDL85e+PlJAQ/ObnV+pxrIwrOh02+vsjPSQEWwMC4CeTIcNgwODUVLzm4YGkkBCcCgpCV+OxWGTMk2ytnT0BAUgLCcFaPz+ojMseSU/Hcm9vpIaE4GBgIKKcnKAHsMR4PgLABFdXyAD8XlBg8T76zfgDwbhiAX/Ta5xs4yhpqh1z5sxBVlaWxWPOnDlW65qCr2VNYmsaLexSySB/eno6Nm/eDACYOHFipdqYP38+VCoVsrKysHPnzkq1UZpKj/DNyMhAnz59cOHCBenD8uTJk1i/fj2+/fZbdOzYEQMHDkSm2S+PV69exYcffog///wT+/fvh6enZ5V3gIiIiIiIiKg+0gJwuXPH5vqHCguRJ4oIkcvxpY+PNAJ0ha8vNqvVuGsw4ERhIbpXYpR/sjHA2V+lktIWeMtkuEehwNPFRg3/rdFALYpor1BgoTH/LAAs9/HBfwoKsNE4uq487ZRKtFMqoRdFaAHc5+yMZ93c8GFODo5ptZhirHfGmO92oEqFF40j4ALlcnzj44Nni92OnaTX46hWCycAK319pdQQz7i7Y0NBAbZrNDij1aJ9OXcapBmDf34VyOnaTqnEwy4u+L2gANMyMjCtWN8eKxZY+lOtxi2zNA8mBzUanNVq0dXOd2uYjuNL7u4YZuzLGFdXPF5QgJ9KGS1dURNdXfGAse0hxvzFc7Oz4SUIeNf4o0GQXI4vfHzQJTkZp4x9Ku4dT0/0NZ6Hj7m6YqtajZV5eWinUGC68Rzwl8vxgZcXHk1Pt2gnzMkJA1Qq7NBosLGgAI+6ukIvivivcSTnY8UCvqZzJM3Ka0F1R2npG6yxJV2DLWkfyvLLL79Aq9UiPDwcvXv3rlQbnp6eaNOmDY4fP46rV69Wqo3SVDrgu2DBApw/fx4A8Nhjj6FPnz64desW/vWvf+HNN99E165dkZ2djeeeew4jRoyATCbDb7/9hpUrV+LcuXNYsGABPvroI7vtCBEREREREZEjSzEGpNo4OVnc7u8sCGjp5IRDhYXSyNSKam5MwbCxoACj09LQVqFAgEyGELkcg52dLSazumG81f60Vms9YC2KSNfrS+Thtawi4uPcXHyZm4ubOh10xcrzzEZbmvapRbE0EdFWgramvukAhCUmWt32FZ2u3ICvaaS0cwXTHazy9YVPZiZ+y8+HQhBQIIrIE0X0UakQZZZG4LJWi9eMA+SWentjvKsr8gwGvJOdjZ/z8/Hk3bu4GBxs1wnvpeNYLMdytEKBn+y0jXbF2jalTmivVFrsSyvja5lhMKDAYIBLscB6iXaM9TsWW97K+Dy+2Hk/0c0NOzQarMnPx6Ourtij0SDVYEB3pbJEuhEXY79s+5mC6oPmzZsDAG7dugWdTmd14rbr169b1K0oUzqHCRMmVOl9qjCewzpd8U/Bqql0wHfDhg0QBAFPP/00vv76a2l5t27dMGbMGPz555+YOXMmli1bJpUNHz4ccrkc3377LX7//XcGfImIiIiIiKjBchME5IaFWS17IDUVm4qNlDWFA5RWggumZZUNGXRRqfCKuzuW5+bivwUF0mhIAFAB+NbXFxOME2xZSzdRXK4owq+M8hV5eSVyEZsz34Ip9CuzIahia9/K42sMQN6t4G3+HjIZvvX1xbe+vtCKIlokJiJPr8cbxXJz/pqfDz2KRv2+YiqTy/G9ry/2ajS4rNPhUGEhetpxlK90HO3WYvlMIf/i25SbvZa2/ERRajultDHGxQXPCwL+KihAhsEgpXMYbyV/c7oxWFyR0dx1Vf3fA/uIjo6GQqGAWq3G8ePH0bVrV4tyrVaLI0eOACiKY1bUtWvXcOjQIQBFAd/K0uv1uHTpEgAgrJRrQWVV+ly4Y/wVr/gsdEOGDJH+HjZsWIn1TImK71TgthUiIiIiIiKihs40YqvQSsDStMx8VJcpGGZrhtalPj5IDg3FjoAA/Ojri8+8vTHK2RkaADMyM6EzbsMUXJ7t4QGxcWOrj6blTNq21hiAe9vTEwVhYdJ6K319S9Q1BS4Mxfa7+HPzvnVVKkvt2yRj4Los/sbgX2oV8rquzMvDDb0e9yoUGFEsncNNY5CxXbGRxgpBwD3GYxdnNuKvvFC3LeMLpeNYbLm1PTQFZO2X3bdmuclkGO3igkIAv+TnY31BAWQAHrUS8DW9xv4OEPClIp6enhg0aBAA4LvvvitRvm7dOmRnZ8PPzw/9+/evcPs//vgjAKBr165o2bJlpfv53XffITMzE3K5vFL9KEulz2atMT9KYGCgxXI3sw9ObytJxn2NH9565kYhIiIiIiIislmwMUXCeZ1OCr4CRekHLhmDg8FmaRRCjX/fKnarcPHb301yDQb4yGS4z9kZE9zc8KKHB/4ICICPceItU2AszNhurJUJkXQ2TgBmSi/wmKtruWkTGhm3d6XYfhyzkv/V1LcLWi2yiwVrbe0b8L9UBEfKmPSpLIWiiPeME4nNNctzbGIaQXyu2D5ozV5L8xGn7sZjdLvYa2d6bT1sCFaWehyt7KPp3InX6y0C66IoIqGexHMmGuNTz2VkIMlgQD+VSjoG5kyvcfGUIVS/vfXWWxAEAd9++y1+/vlnafmpU6fwyiuvAABmz54NpdmPLp988gnCw8Mxbty4Mttes2YNAODJJ58ss152djbGjx+Pf/75x2K5Xq/HihUrMHPmTADA1KlTERoaavvO2aDGf76wZ/4ZIiIiIiIiooaiu1IJD0FAvF6PFzIycFunw02dDs/evYu7BgP8ZDKLvLYdjX9/npsrjRa9pdPhQ2Mg0tyfBQVonJCARdnZUq5gvSjix7w8ZBgMUALwMQYVBzs7QwFgr0aDWZmZiNPpkKbX4/OcHEQlJuJKKRNxmYswBteWZGfjhk6HXIMBV7RafJubW6JuB2PwdbtGgy9zcpCm1+NUYSFmWpmQqYmTE9oqFMgRRYxNT8epwkJkGgzYXFCAlomJ+I+Nk5MNMKZS2K3R2FS/uO/y8nBLr0cnhUKaxMzcUOOEZL/k5+OTnBwk6fW4ptNh8t27uKPXw00Q0MssnYNpIr5PcnJwQKNBhsGAnWo1vjAer27l5CQG/nccP8nJwTa1GpkGA9bn5+NXs/QdJk3kcvjIZMgSRSzMzoYoihBFEYtyclD+q1s3DFSppMA1AIyzMroXAPYYX+P7jK8JOYZevXph4cKFMBgMePzxx9GsWTN06NABnTp1QnJyMu6//368+uqrFutkZmbi5s2bSEpKKrXdQ4cO4erVq1AoFOUGhg0GA9auXYtu3brBx8cHnTp1QteuXeHv749nn30WarUaw4cPx6effmqXfTbH8epERERERERE9YC7TIZ3jaNFv87LQ5PERIQnJuIHYxDzAy8vqMwGWT3j5oYAmQw39Ho0S0xEUHw8IhITraaE2FBQgExRxJysLAQnJMA3Ph6ud+5g4t27AICn3d2lkbiN5HLMN/bjo5wcRCYmIiAhAS9mZuKmXo9/WQnaFvesuzsA4Pv8fEQkJsIjPh4tkpJwwMpo064qFe4zBjynZ2YiICEBHZOTpdG8xS339oYSwFa1Gh2Tk+ETH4/709JwXa/Houxsq6kgiuurUkEJ4KBGI00EZyuNKOL9Mkb3AkXBxcddXWEA8HJmJholJCAqMRE/GV/Lj729LUbt/p+bG0LkctzS69E7JQW+8fEYmJqKFIMBEXI5ptiQpuL/3N3hK5MhzWDA0NRU+MTH4+H0dDSxchydBAGzjbmF52VnwzM+Hl7x8ZiflVWhY1GbZIKAx82CvGOsBN73aTS4rdejkUyG1sUmhKuPBAd+VMZbb72FjRs34r777kN6ejquXr2Kdu3a4ZNPPsEff/wBeRkTS5bGlM5h2LBh8Pf3L7Oum5sbFi9ejIceegj+/v64du0aTp48CWdnZ9x///345ZdfsGnTJjhXw48NVR6vHhsbW6Gyw4cPV3WTRERERERERA3SDA8PBMrlWJaTgzNaLWQA2isUmOXhgYeLjWD0kMmwPSAAL2dm4lBhIfQoChy2VyrxXLHRsd/4+mK0iwtW5OUhVqNBmsEApSCgrZMTJri5YYYxQGsyx9MTzZyc8GlODk5ptRABRCsUmO7ujvE2BB8fdHHBL35+WJaTg0vG9fuqVOisVGKelRHIv/j54eXMTPxZUAA9gJEuLpjo6ophaWlwKnYncX9nZxwMCsLC7Gzs12iQYzAgyrgfM93dbZr8zUMmwwQ3N/w7Lw9Lc3Kw3Men3HVMvs3NxR29HtEKBUZZCTKa/Ojri+5KJf6dl4fLOh2UADorlZjt6YkhxQJA/nI5DgYG4q2sLPytViPdYIC/TIZhzs5439sbnjakdPAyng+vZmYitrAQHoKA593d4SYImGUlkDvbwwOFoogVeXlI1evRWqHAR97eeOruXSkHcV3X3DiSfISzM/ysBPdMo92fL3Z+k+N44IEH8MADD9hUd968eZg3b16Zdb788kt8+eWXNrWnUCgwa9Ysm+ramyCKFUhiY0Ymk1U6PYMoihAEweHz+GZnZ8PLywsnTpyAR7EZOcl+NBqNNAx/2bJlFvlXiOozQRAQGRmJ69evo5If1WSjnJwcREdHIysrC56enrXdnTqJ17Saxfc/ORp+X6tZvK7ZqEmT2u4BVdEXOTl4ITMTfVQq7C02v5A9XNFqcU9SEpSCgCOBgWjLz65655WMDHycm4sffH3xZLEfIrap1RiWmgoPQcDNkBB41+akbbdu2aWZ/Q6cRrU3vxNXSJXOZlMOl4o+iIiIiIiIiIhsYRBFvJ2VhQEpKThdWIi7ej3+m58vjQQeWk25V5srFJjh7g61KGL83btQM55Rb6hFETvVanyXlwdnQcBDxUZap+n1mJSeDhHAQi+v2g32ElWDSqd0MBSb7ZKIiIiIiIiIyN5yRRFr8/NxVadDh+Rki7IWTk54sRpvx//Q2xv7NRp0M+b0pfph2t27+N6YD/lpNzeLfMhAUT7s7ioVRBSlSSFyNFXO4UtEREREREREVF08ZTIcMuav3VhQgDSDAY3kcoxyccE8T0+b8tdWllIQ8HdgIEeA1jMCAB+ZDMOdnbHU27tEubMg4Dc/P+Q72Kjtik9BRo6qwgFfrVaLxMREAEATO+Q8snd7RERERERERORY/OVyfO3ri69rYdsM9tY/K/38yq0jFwR4OHDOW2rYKhzwPX78OHr06AGZTAadTlflDti7PSIiIiIiIiIiIqKGqsIBX41GA6Bowrbbt29XeRK2GzduSO0RERERERERERERUeVVOOB77tw56e/w8HB79oWIiIiIiIiIqE4qMBiwIDsb6woKcEung6sgoIdKhXmenuimUlWoLb0oonliIuL0emwPCMBAZ2e71qeGiclHyKTCAd9OnToB4IhcIiIiIiIiImo4RqenY6taLT3PEkVsUauxU63GwaAgdFYqbW5rfUEB4vR6tFMobAreVrQ+ETVsFQ7+d+vWDQaDwe4PvV5f6Z2Ii4vD2LFjERwcDB8fH/Tq1QubN2+2WsfPzw9KpRJRUVGYP3++1bzBv/32G7p06QIXFxe4u7ujb9++2L59e6X7R0REZCte04iIiIjqngMaDbaq1XACsMbXF2khITgbHIweSiUKAbyXnV2h9pbl5AAAXnZ3r5b6RNSw1fvR3qmpqejRowfWrVuH/Px8uLi44ODBg7j//vvxyy+/AAAKCgowaNAgrFu3Dmq1GkFBQYiLi8O8efMQExNj0d7WrVvx6KOP4ujRo/Dy8oJSqcS+ffswfPhwnDlzpjZ2kYiIGghe04iIiIiq5oZOB+H2bQxMSUG6Xo+XMjIQnpAAp9u3MSszs9LtHi4sBACMcHbG425u8JPL0UahwCIvL4tyW8RqNDhUWIhAmQyPu7nZvT4RUb0P+C5atAjJycl49NFHkZqaioSEBHz55ZcAgGXLlgEA1qxZg+vXr6N///5ISUnB7du3ceTIEahUKixbtgz5+flSewsXLgQAfP7550hKSkJ6ejpmzZoFnU6HRYsW1fwOEhFRg8FrGhEREVHVnDQGXp0FAe2Tk/FLfj66KpUY6uyMbhVIuVBcjsEAAGjsZJkZs4nxebax3BZLjaN1n3N3h0oQ7F6fGi6ZAz+oYur9Mbt79y569eqFhQsXQmVMkv7kk08CKLrlFQDOnj0LAJgyZQrcjL+GderUCT179oRWq8Xly5el9s6ePQuFQoFp06YBAARBwAsvvADAcsI6IiIie+M1jYiIiKhqTmq1AIDNajUedHbGjZAQ/Orvj00BAXjE1dXqOlpRhLqUh8E4f1F5sxjZOsvRDZ0O6wsKoEJRANfe9YmIgEpM2lbXrFy5ssSyixcvAgBCQkIAAJnG2zY8PT0t6pmeZ5rd1pGVlQUvLy/I5fIy6xEREdkbr2lEREREVXPKGPDto1Lhcx8fyGwYEfvM3bv43uwuKXO7AgLQ346TpH2SkwM9gIlubgiSy3FOq8XcrCw0kcuxzMenyvWJiAAHCPha8/777wP436iomqLRaKDRaKTn2cak7YIgQOBtF9XG/NjyWJMjMZ3LPKerX10+xrymNUx8/5Oj4fe1msXjSw2ZKaXDR15eNgV7a1KWwYB/5+UBAF4yjtZN1evxn4ICtHEqGZ6paH0iIhOH+4RYvXo11q9fj4iICOm21ZrywQcfYP78+SWWR0RElBiJRfajVqulv8PDw+Fsx19fqeEQRdEiuFXbTP3JzMxEcHBwnfuPm0qlqnN9qorsCs6qXFN4TaOIiIja7gKRXfD7Ws2qq9c1ouqWZTDghl6PMLkcXSqQr3eFry++8vW1Wlb5rL9WtpObixxRxH0qFdrb0L+K1iciMnGogO+FCxcwbdo0yOVy/Pjjj1L+w5oyZ84cvPLKK9Lz7OxsNG7cGHFxcfDw8KjRvjQk5kG6GzduQMkLYZ0niiIKKzCLbU3QaDSYM2dObXej3vjggw9q/DO2LEqlskoB6BzjRBh1Ca9pDZsgCIiIiEBcXBxE0dasgER1F7+v1ay6eF0jqgmnjP/H6FvBwQk1kdJBJ4r4LDcXAPCyDd+lKlqfCHCAibrIbhwm4Jufn49HH30UeXl5mD9/Pnr16lXjfVCpVFb/Qy6KIv+zVo3Mjy2Pdf2g0Wjw6quv1nY3qArqWnB86dKlVQqI1rXPDV7Tak5d/AFKFEVotVpkZmaioKCgtrtjoao/rlDDxe9rNYvHlxoq04RtHRUKu7dd3tWvvPJ1+fm4rdejuZMT7rchgFzR+kRE5mos4JuQkIC3334bly9fRnBwMNq2bYsOHTqgY8eOCA8Pr3L7zz//PM6dO4e+ffvirbfeqnqHiYiIagmvaTWnsLCQP0BVQFV/XCEiIqpOpoBvdAXvIljl54dVfn5l1nE3/uB5W6ezWH7L+NyjnB9EtxlT21zR6SC7c6dE+TmdDuEJCbhhnKi3ovWJiMzVWMB3woQJOH78OB588EGkpaXhm2++QWJiIgRBgKenJzp06IDdu3dXqu1///vf+P777+Hj44PVq1dbzEYOAN7e3gBK5rIyPfcxm9nSy8sL+fn50Ov1UjvW6hGRfYgdHgNkDnOzgf2IImDQF/0tkwMcUVeSQQfh1P+zd9/hUVRvG8e/m7KbXkjoBEKXIghIlSpIU3hFUKoCYkFAFCwIogSxID8pCtgRLIgFRVDpCKJ0qdJ7byEJSUjZtH3/CLtmSUIKSTYJ9+e6cpGdOXPmmc0Jk33mzDPfOzqKPKdzmoiIiEjuWEs65McM32bXL3gujY/n25gYOrm5cTElhVciIwFoesMF0QSLhRTAFXA2GHAyGHAmveQ037uk+Zs/p+1FRNIqsCzLli1b+Oyzz+jXr59t2ZUrV9i1axc7d+5kz549uep37969tgfZzJkzh6CgoHRt6tSpA8DcuXPp2bMnnp6e7Nixg40bN+Lq6kr16tXt2m7cuJGPP/6Y4cOHY7FYmDVrll0/IpKHnFzAOe//IBMpinROK3hGo5GpU6c6Ogw7ZrOZcePGAan1sgtTrdPCFIuIiEhaSRYL+xITKe/sTKBzRqnSW3OPyUQnNzdWxMfTPzzcbp0RGH/DQ207hobyp9nM3BIlGOTpyZwSJZiTwYPh1sXH0y40lDouLuwtW9a2PKftRUA1fOU/BZbwDQoKosQN/1kFBgbSoUMHOnTokOt+X375ZeLi4nBxcWHs2LHp6koePHiQAQMG8Pbbb7Nu3TpKlSpFQEAA586dIyUlhTFjxuDh4WFrP378eLp27cqIESOYNGkSiYmJhIeH4+LiwpgxY3Idp4iISFZ0Tit4BoOhUJcoMJlMSrKKiIhkw8HERMzkz+xeq0UBAUyMirLV13U3GGhuMhHi40Mjna9FpBApsITvsGHDWLBgAZ07d87TfmOvP0kzKSmJQ4cOZdjG3d2dVatWMWbMGNauXcuFCxcIDg7mscceS1cbsUuXLixYsIApU6awf/9+nJ2dadmyJRMmTODOO+/M09hFRETS0jlNREREJHfqGo1YMrg7Ki+5Ozkx2c+PyddLbN3MulKlstVnWze3HMWd0/YicnsqsIRvWFgYf//9N6NHj+bVV18lIIuC6NmV3bq/1apV46effspW2z59+tCnT59biEpERCTndE4TERERERGRW1VgCd9PP/2US5cuMWPGDGbNmkWDBg3svurVq4ebm1tBhSMiIiIiIiIiIlJsqIavWBVYwvfChQuEhoaye/dudu/ezZ49e9i8eTNz584lMTERZ2dnEhMTCyocERERERERERERkWKnwBK+ACVLlkz3kLakpCT279/Pnj17CjIUEREREZE8YbFYSEhIcHQYhZrZbLb73mKxODCaws9oNGIwGBwdhoiIiBRRBZrwzTAAFxfq1atHvXr1HB2KiIiIiEiOJSQk8MILLzg6jCJj7Nixjg6h0Js6dSomk8nRYYiIiEgRle8J32vXrrF9+3ZcXV25++67MRqNGba7cOECZcuWze9wREREREREREREih3V8BWrfE34bt++nfvvv5/Q0FAAypcvz48//kjTpk0B2LFjB0uWLOG3335j586dJCcn52c4IiIiIiL5ylK/Nzg5/Ca6wsdigZTrf+s7OYPKFaSXkoRh9/eOjkLkthMSGcnEqCiGe3kxy9/f0eFkS1xKCm9ERfFjXBynk5LwMBhobjIR4uND02zcHZBssTAtOpq5MTEcT0rC1WCggdHIOG9vOru727VdFx/P4+HhnEhO5n43N34rWTK/DktE8lC+/jX60ksvATB9+nRKlSrFunXr6NmzJ9988w0jRozgwIEDWCwW7rzzTl599dX8DEVEREREJP85uYCzq6OjEBGRYqxHWBgr4uNtryMtFpbHx/NHfDwbS5emUSZ3VlsNj4jgk5gY22uzxcJfZjNdzWYWBwbS7XrS98uYGAaHh6Oq6yJFT74mfHfs2MGHH35Iv379AOjTpw8zZsyga9euuLi48MYbbzBgwAAqVaqUn2GIiIiIiIiIiBR5G8xmVsTH4wJ8WaIEndzcuJiSwpPh4WxKSOCtqCh+DgzMdPszSUl8dj3ZO83PjwEeHkRbLIyKiGBJfDwTIiNtCd8TSUkEOjnR0mRiUVxcQRyeiOSRfC3vERUVRbVq1eyWDRgwgPj4eGbOnMmrr76qZK+IiIiIiIiISDZsSUgAoKubG/08PQlwdqaOqyuTfX3t1mdmW0ICKUA9V1dGeXtT0tmZKi4uzLxezmJXYiJmS+qc3s5ubhwqW5buN5R5EJHCL9/rORtuqM/lf/0/kTp16uT3rkVERERERESkgM2LicFw5gwPhIayMj6eBhcv4nbmDHdcuMA3aUoJZFeSxULpc+cwnDnDVrPZbl2ixULA9XXb0qxbEx9P59BQSpw7h+nMGWpduMC7UVEkW7JXoCAkMhLDmTOMiIiwW34yKQnDmTN4nT2bbpvDiYn0vnKFwOv7vPPiRb64di3Hx3sz0SkpAAS52N+wXfH666jr6zPd/vrxBzk7Z7i9Bbh2vY9mJhP+TnoMWFFiKMZfkjP5/kSJXr16ceedd1K7dm3q1KlDjRo1MBgMON/wn4uIiIiIiIiIFB/bEhK4PzSUpOuvDyUl8Wh4OOWdnWnn5pbtflwMBnp7eDDz2jV+iIujSZoHk62Jjyc8JYUaLi40vr58RVwcna9csevjYFISr0RGcigpiS9KlLjlY7vRkcREml66xNU0CeW9iYkMiYjgmsXCSG/vm26fYrGQ2dxcZ8D1+mS6rNLVt7o+u21EpHDL14TvO++8w+7du9m9ezcrVqwgOTkZg8GAxWKhX79+NGnShIYNG9KwYUMaNGiAl5dXfoYjIiIiIiIiIgXkckoKk319edTTk9DkZIaEh7M9MZEp0dE5SvgCPHo94ftjbCz/8/W13U38w/Xaso96eNjadnJ3Z3FgIJEpKbQxmXA3GJgfG8uoq1eZFxPDeB8fqrjkbTpkXGQkVy0WGri68rG/P1VdXFhlNjMoLIxXIyN53NMTr5vMll1vNtMuNDTDdQM9PJgXEJCn8YpI8ZavCd8xY8bYvjebzezdu9eWAN69eze//vorX3/9NQBOTk4kJSVl1pWIiIiIiIiIFCEVnJ0Z4+MDQDlnZ6b7+9P68mX+TUzMcV+NTSZqurhwKCmJLQkJNDOZSLRYWBwXhwHo7+lp195adzbRYiEZGOrlxQ+xsWxKSGBHQkKeJnyTLRaWxccDMKdECWq5ugLwoLs7Azw9mRMTwx9ms2rhikiBuaX/4TZu3EhCQgJ33XUXfn5+N21rMplo1KgRjRo1slt++vRpdu3axZ49e24lFBEREREREREpROpfT3xa1bieZL2QnGy3PD6TuroGwJTmuUD9PTx4PSqKH2JjaWYy2co53GM0UjlNAvdqSgrjrl7ll7g4LmRQ0zYmm3V8sys0JcXWZ8NLlzJscyQxEW6S8G1tMhFXoUKG61QQU7JLY0Wscp3wDQsL44EHHiAyMpJp06bx3HPP5aqfihUrUrFiRbp3757bUERERERERESkkLMmo25Mwbpn8AA0gErOzpwsV872eoCnJxOiolgYF8dUP7//yjncMLt3WEQEC2JjM40jr2vUZpawTutaFm1U0kFE8lKuE75z587l6tWrdO/ePdvJ3mPHjlGiRAn8/f1zu1sRERERERERKQJuTHFaE72GGxtmU2UXF1oYjWxISOAvs5nFcXEYgUfS1O9NtlhYGBuLE/BbYCAd3dxwvj5LeFBYGF/eJBGcljU5nZ3ksPF6/6WcnLhUvnz2DygXsnrvbnV9dtuISOGW64Tv77//jsFgICQkJNvbXLlyhZo1a1K1alX27t2L6w23d4iIiIiIiIhI8bD7hlq9R64/t6ecs/2N55agoGz3OcDTkw0JCTx79SrhKSk86O6Of5qHoYWlpJAIBDo50eUWauaWvx7j6RueNXTuhnIUXN+XidSH1J1ISrIrL5FkseBiyDqF2tbNLVvvg9f1vs7cEJc1Tu8s9mXb/objsG5vgJs+XE5EioZc/xbv3buX0qVLc9ddd2V7m6ZNm/Lwww9z9OhRVq1aldtdi4iIiIiIiEghdy45mbeioriQnMy/CQmMunoVgDtvYfLXI+7uGIE915PJj6aZ3QtQ0skJT4OBKykpfBAdTWhyMlEpKWwzm1ly/cFqaVkToCeSkkhIU3bhLqMRgOXx8ay6vl1sSgovXT+GtIwGA/e5uaXGd+UKm8xmrqak8LfZTKNLl5gRHZ3r471RM5MJgKXx8XwbE0NYcjL7EhN5JTISgKbX1wOkWCzEWyyY0xxXE6MRJ1Lfv+nX35/jSUmMvH5c9V1d7eomS9HiVIy/JGdy/Z5FRUVRIZOC4jczYsQILBYLixYtyu2uRURERERERKSQK+fszOuRkZQ7f556ly6xLSEBgHE+Prnus4SzM12vz9z1Mxi4/4ZZvAaDgSeu1/R97upVSp0/j++5czS5fJmIDB7g1uR6YndpfDyms2fZdT3GhkYjndzcSAI6hoYScO4c/ufOseP6+htN8fPD12Dgn8REWly+jP+5c7S6fNmWWI3OYN+5cY/JZIurf3g4gefPU/fiRTYlJGAExqd5b9ebzbifPUvNCxdsy4JcXGzvz+jr70/VCxdYHBeHAZjo65sncYqIY+U64RsQEEB4eHiOt2vWrBleXl5s2bIlt7sWERERERERkUKugasrPwQEUMPFBSNQx8WF7wMCaJVmFmpuWGf1PuzhkeFs1Ml+frzl60stFxc8DAbKODkxwsuLPjfMBgZo7ebG6z4+BGZQxuDbEiV4zMMDfycnzBYL7d3cWF6yZIYx1XJ1ZVvp0vT38KC0kxOuQBVnZ17x9mZn6dJ452GZhEUBAYzx9qaKszOugI/BQCc3N/4sVYpG1xPYN/Ohvz+Tr78/RsDTYKCVycTvgYF0v4UyGCJSeOS6hm+FChXYvXs3165dw8vLK9vbOTs7ExwczOnTp3O7axEREREREREpAnp6eNAzg0TrrXjIwwPLTfp0MxgY5+OT4UziBQEB6ZZN9PXNcGZrCWdnvsygfWa1dqu7uvJNBu3zmruTE5P9/Jjs53fTdpnVBXY2GBjj48OYbM60HuTpyaDrs4JFpGjI9SWmTp06kZSUxOeff57jbT08PIiLi8vtrkVERERERERERCQNR9fZVQ3fwiPX71n//v1xcnLijTfe4ODBgzna9ty5c/iqLoyIiIiIiIiIiIhInsp1wveOO+7g2Wef5erVq3Tu3DnbNXl37NjBuXPnqFOnTm53LSIiIiIiIiIiIiIZuKVZ0W+//TYdOnTg9OnTtGrVihdffJFLly5l2j4iIoInn3wSg8FAjx49bmXXIiIiIiIiIlIIDfL0xBIUxG+ZPOBMRETyV64f2gbg5ubGr7/+yqBBg/j++++ZPn06s2bNol27drRt25aaNWvi6+tLdHQ0W7duZc6cOVy6dImqVavy5JNP5tUxiIiIiIiIiIiIiAi3mPAFMJlMLFiwgIceeoixY8dy/PhxVqxYwcqVK9O1tVgsVKpUiSVLluDu7n6ruxYRERERERERERH0cDP5T56NhYcffpgjR46waNEiBg8eTJUqVXB1dcViseDs7EzdunWZOHEiu3btolatWnm1WxERERERERERERG57pZn+KZlMBj4v//7P/7v//7PtiwuLk6zeUVEREREREREREQKQL7P9layV0RERERERERERKRg5OkMXxERERERERERESl4quErVhoLIiIiIiIiIiIiIsWEEr4iIiIiIiIiIiIixYQSviIiIiIiIiIiIiLFhGr4ioiIiIiIiIiIFHGa1SlWGgsiIiIiIiIiIiIixYQSviIiIiIiIiIiIiLFhEo6FHIWi4WEhARHh1Gomc1mu+8tFosDoyn8jEYjBoPB0WGIiIgUG3Z/eyQnOS4QKdrSjB39PSsiIiK3QgnfQi4hIYEXXnjB0WEUGWPHjnV0CIXe1KlTMZlMDo1BH4wlT+iDsYgUEmkvzhv2fO/ASKS4SEhIwM3NzdFhiIiISBGlhK+IFDh9MJa8pg/GIiIiIiJyu1PdVrFSwrcIGXMlHKNmsaVjARKvf+8KqFhBegkGA+8GlnB0GCIiIsWS0Wi0fW+p1xuc9Se25EJyku1CeNoxJSIiIpJT+mu0CDFaLOhPv4w5tkBBEVDILhTog7HkCX0wFpFCwq42vrMLOLs6LhgpFvS8BREREbkVyrKISIHTB2PJa/pgLCIiIiIiIpJKCV8REREREREREZEiTtNgxEr1nEVERERERERERESKCSV8RURERERERERERIoJJXxFREREREREREREignV8BURERERERERESninB0dgBQamuErIiIiIiIiIiIiUkwo4SsiIiIiIiIiIiJSTCjhKyIiIiIiIiIiIlJMqIaviIiIFBiLxUJCQoKjwyjUzGaz3fcWi8WB0RRuRqMRg8Hg6DBERERECgXN6hQrJXxFRESkwCQkJPDCCy84OowiY+zYsY4OoVCbOnUqJpPJ0WGIiIiIiBQqSv6LiIiIiIiIiIiIFBOa4SsiIiIOMeZKOEaVK0jHAiRe/94VUMECewkGA+8GlnB0GCIiIiIihZYSviIiIuIQRosFo6ODKKRUpOAmdJFARPLI6F6nHR2CiAgA0xwdgBQ7SviKiIiIiIiIiIgUcarbKlYaCyIiIiIiIiIiIiLFRLFL+O7du5eGDRtiMBh48MEHbcuTk5N58803qVatGkajkYCAAHr16sWxY8fS9XHixAkeeeQRAgICMBqNVKtWjYkTJ5KUlFSARyIiIqLzmoiIiIiIiORMsSrpcOzYMe6++27MZnO6dePHj2fy5Mk4OTlRrlw5wsLC+Omnn9i+fTv79+/H3d0dgLi4ODp06MDx48fx8PCgdOnSnDhxgpCQEOLi4pg8eXJBH5aIiNymdF4TERERERGRnCpWM3yjo6MpXbo0Y8aMsVseGxvL1KlTMZlMbNu2jTNnzhAaGkrbtm05efIk33zzja3t/PnzOX78OG3btuXy5cucOXOGbdu2YTKZmDZtGrGxsQV9WCIicpvSeU1ERERERLLLqRh/Sc4Uq/esSpUq7N+/n86dO9stP3z4MImJibRo0YKGDRsC4OnpyeDBgwHYt2+fre3evXsBGDx4MJ6engA0bNiQFi1akJiYyOHDhwviUGwsaZ5EnaAvfd3Cl5VFTzcXKTKK43lNRERERERE8lexKung4+OT4fKrV69muN762ro+p20LQkLCf6m6d0sGFOi+pXhKSEjAzc3N0WGISDYUx/OaSLGXotrYGbJYICU59XsnZzAYHBtPYaSxIyIiInmkWCV8RUREREQcybD7e0eHICIiIiK3OSV885DZbLZ7sE5UVBQABoMBQy5nMZhMJtv3Y0LDMN5aiHKbSuC/GeImkynX4zGvOHr/Uvzcyv+z1u3FXn6c06zbi+SVWx2PeRmHSF7S/7UiIpIbxapuq9wSJXzz0DvvvMPEiRPTLa9cuXKmt+VmJT4+3va98fqXyK2oUqWKw0s6pB3XInmhcuXKtzSurclM+U9+nNNAv/+St271dz+vWCwWFi1a5OgwCrX4+Hj69u0LwIIFCwrFz60wu9UL9DqviYiI3N6U8M1DY8eOZfTo0bbXUVFRBAUFceLECby9vXPVZ9rZVSJ54cSJE3Yzxx1B41ry2q2O6+jo6DyMpnjIj3Ma6Pdf8lZhOKdJ9qT93b948SJGo6Yx5Ced10RERG5vSvjmIZPJlOGHDovFgsViyVWfud1OJDO3Mh7zMgaRvHSr41pjMr38OKdZtxfJK4XhnCbZk/bnpJ9b/tP7KyIicnu7Lcp7+Pn5AelvbbK+9vf3z1VbERERR9B5TURERERERDJzWyR8q1evjqurKxs3bmTHjh0AxMTE8MUXXwBQp04dW1vr93PnziUmJgaAHTt2sHHjRlxdXalevXoBRy8iImJP5zUREREREbmRUzH+kpy5LUo6eHp6MmrUKKZMmULjxo0pX748YWFhxMbGEhwcTP/+/W1tBwwYwNtvv826desoVaoUAQEBnDt3jpSUFMaMGYOHh4cDj0SkGEpJcnQEhZPFAinJqd87OYOetp3ebTx2dF4TERERERGRzNwWCV+At956C3d3d7766ivOnDmDt7c3nTt3ZsqUKbi7u9vaubu7s2rVKsaMGcPatWu5cOECwcHBPPbYY7z66qsOPAKR4smw+3tHhyBSJOm8JiIiIiIiIhkplgnftm3bpntQgYuLCyEhIYSEhGS5fbVq1fjpp5/yKToREZGc0XlNREREREREsqtYJnxFpHAzGo1MnTrV0WEUamazmXHjxgHwzjvvYDQaHRxR4ab3R0REREREbneqdStWSviKSIEzGAyYTCZHh1FkmEwmJTRFREREREREJFuU/BcREREREREREREpJpTwFRERERERERERESkmVNJBRERERERERESkiNOsTrFSwldEREQKjMVisX2f4MA4pOhKO27SjicREREREUmlhK+IiIgUmISE/9J175YMcGAkUhwkJCTg5ubm6DBERERERAoVzfYWERERERERERERKSY0w1dEREQKjNFotH0/JjQM403aimQkgf9mh6cdTyIiIiK3O83qFCslfEVERKTAGAwG2/fG618iuZV2PImIiIiISCol/0VERERERERERESKCSV8RURERERERERERIoJJXxFREREREREREREignV8BURERERERERESniNKtTrDQWRERERERERERERIoJJXxFRERERERECrHIyJNMn25g1iyvbLXft28e06cb+OWXB/KlfU7kZ9/5KSUliR073mf+/EbMnu3Dhx/68/33Ldm3b56jQ7tlhw8vZPp0Az/+2NbRoYhIPlFJBxEREREREZHbwJw5wURFnaJv322UKXO3o8MptJKTE/j5506cPbvObvn58xs4f34Dp06tpEuX+RgMBscEKCKSBSV8RURERERERIqRWrUepWbNPhgMzo4OpUjatCmEs2fXYTT60KrV/6hcuSvJyQkcPvw9mzeHcOjQAoKC2nHnnU86OlQRO7qNX6w0FkRERERERESKEScnZ1xc3HB2dnV0KEVOcnIi//77KQD33fc59eo9hbd3Bfz8qtCkyViaNZsIwK5dsxwZpojITWmGr4iIiIiIiEgxsm/fPFauHEzlyvfz4IO/AfDhh36YzZEALFjQmJIl6zNgwK502549+yfr17/ElSv/4uVVnkaNRlO//rB07SIiDrNx42ucOfMHZnMk3t5BVK/ei2bNJuDq6pGtOJOTE9i69R0OHpxPdPQpXF29KF++Fc2bT6Rkyfp2bVNSktiwYTz7939JQkIU5cu3pF27Wcyf34DExBgef/wEvr7BLFrUlZMnl9Ghw2fceecTdn18/XU9rlz5lx49lhMc3CnDmCIjjxMfH4arqyfVq/dKt75u3cfZsGEsV67sISnJjIuLKVvHmlZSUhxbt77D4cM/EBV1AhcXT8qUaUKjRqOpVKmjrZ3ZHMW2bZM5cmQh0dGncHHxpEKF1jRv/gYlS9bL1r4iIg6zbt1znD27HqPRizp1Hk/33qYe90m++KIyrq6ejBhxzW7djz+25ezZP7n//h+pUSP1Pdm0KYTNmydSv/5wgoLuZePG14iMPEaJEnfQsuW7BAd3IjR0N2vXjuTSpW14eJSifv3h3H33S3Z9r1gxiP37v6RVq/9hNHqzbdu7xMRcoFSphrRt+z5lytzNmTNrWb/+JcLC9uLjE0yTJuOoXfsxWx+3+jMXKY6U8BURERERERERLl/eyc8/dyQ5OQGAyMhj/PHHcNzcAqhZs7et3dWrx1iwoClm81XbssjI4/zzzxQuXNhEr15rcXLKupzEb789zPHjS2yvk5PDOXZsMadOraJPn412icnNmyfyzz/v2l6fOrWSH35oTWJijF2ftWs/xsmTyzh4cL5d8i8i4ghXrvyLu3sgFSu2zzSm2NjLAHh7V8ywRq+HRylcXNxJSoojLu4y3t5BWR5nWsnJifz0U0fOn/87zbIETp1awalTK+jS5VvuuKMvFouFX37pyvnzG+zaHTu2mNOnV9O371YCAmpnua+ff+5MVNQJAJKSYtm2bTImk1+OYr6ZkyeXs3v3h4AFgNDQ3Sxe3I0ePZbz2289bWMkKuoUf/31MkajL/XqPZWun3///ZSrV4/YXl+4sJGff+7I/ff/wOLF99vGZETEIVasGIiHR2lbAvdWf+YixZFKOoiIiIiIiIgUc089dQkPj9IAPPLIX/TtuzVdm5iY87Ro8SZPP32Jxx7bT1DQvQBs3fqWXbvNmydiNl/F378GvXtvZOjQUDp1+gpnZxPnzv3F0aM/ZxnP6dN/cPz4EgwGJ+6990OefvoS/fptp1SpBiQlxbJhwzhb2+TkBHbufB+AWrUe44knTjNo0CHKlbsnXb9Vqz6IyeTL2bN/Eh191rb8yJEfAahevRdOTpnPfbNYkgEwGDJPl1jXpaQkZ3mcNzp0aAHnz/+Ns7OJ9u0/4amnLjJ48DHq1h1CUNC9VKnywPV9GOjWbRGtW79H375beOaZMAYM2EWpUqkzmrdtezeLPcGRIwuJijqBs7OJrl0XMHRoKD17rrZL1N+qyMhjdOo0j6FDr9CnzyZ8fauQkpLITz+1p2rVB3nyyXMMHnyEypW7ArBt2zsZ9nP16hH+7/9+5ZlnwnjooRW4uQVgNkfw88/30ajRizz11EUGDNhNmTJNrvcz2bbtrf7MixOnYvwlOaP3TERERERERKSYc3Ex4eLiBoCzsxvOzsZ0bXx8grn77pfw8ChFQEAt7rtvDgBhYftJTk60tTt9ejUAbdrMoFy55ri7B1K79qPUqfP49fVrsozH2kf16r2oX/8ZPDxKUbp0Q9q3/xiAM2fWYrGkzhqNijpJQkI0rq6edOjwCd7eQfj71+Dee2dncJxu10sxWDh0aIFt+ZEjCwGoWbNPlrHlp1OnVgBQr94z1Kv3FJ6epfHzq8J9931Oz56rMBq9bW09PErSqNELlC7dGBcXD/z9a9KixZsAXL68Pct9XbnyLwC1ag2gZs0+tpmuTZqMy2LL7CtfvjW1az+Gu3sAZcs2o1mzCbZ19947Gy+vcvj5VaNjx3lA6s/SbI5K10+tWo9RpcoDuLmVoFKljjRoMBIAk8mXFi3exNOzNCVL1qNdu9SfeWjobtu2hf1nLo61dOlSOnToQIkSJfD09KRhw4bMnDmTlJSUHPUTEhKCwWC46dfBgwfz6Shy7va4xCEiIiIiIiIiNxUQUMfuta9vMM7ObiQnxxMbe9FWvsBa9uDGWrAlS951ff3FLPcVF3fzPpKS4jCbI3Fz8yMmJrU/b++KtqQ1gKdnaVxdPdOVdahV6zH27p3DwYPzufvul7h69TiXL+/E07Mc5cu3yjK23EhJSSIlJSnDdU5OrrYSF9ZjKV367nTtbpxVfOLEUjZufJ3w8P0kJcXZrbvxmDNi/Tn4+dWwW16yZIMst82uwMA77V77+9cEwNs7yK6Ws4dHSdzdA4mLu8K1a+cwmXyy1U9gYD270hoBAbUAMJsjSEqKw8XFHXDMz1wKv8mTJzN27FgAqlSpgpeXF7t372bkyJGsXr2aRYsW4eSUs7mwQUFBVKxYMcN1Hh7Zq19eEJTwFREREREREZEMOTk5k5xsX77AWvbgxlnC1teZJT7TsrZxcsq4j9T9JF3/N3Um3s3KLKRVvnwrfHyCCQ3dzZUr+zhx4ncAatbsnWUfBoOz3T6trl49RmzsZcqVa25bl7ZO8ZYtb7J588QM++zYcS516gy63q/1vbv5w96uXNnHkiUPkpKSmEkLy023T3sM2X3f8oL1/cton/+9t1mXwsisH+tysB+Tt/Izl+Jp06ZNjBs3DicnJ7755hv69u0LwO7du+nUqRNLlixh2rRpvPjiiznq9/HHHyckJCQfIs5bGvUiIiIiIiIikm3WpJv1QVpW1tfZqZdqbZOSknEfqftxuf5vaurixiRsZssMBgO1ag0A4ODB+Tm6td/DoxQA0dGnbSUlLJYUfvutFwsXtmXTphDbbFt391JZ9pc+ttRjSk4237Td0aM/kZKSSHBwZ4YODWXUKAujRll4/PETOdhXxu9bRu+ZNXltPeai5lZ+5sWJo+vsFqYavm+++SYWi4UnnnjCluwFqF+/PtOmTQNSZwAnJmZ2UaVoU8JXRERERERERLLN07MMAFeu7LFbfuVKal1VD48yWfZhbRMaat+HtTari4s7JpPv9f2VBSA6+gxJSf8lSlNf25c6sKpV6zEg9SFhly5tw9e3iu2BXzfj61sFN7cAEhNjOHw49aFfBoMTrVr9D1dXb9ssXj+/6ri4/DdLt3nzEFtS9sYv6+zetMdy6dI/6fadNtltLf1QufIDuLsHZhl3Rqz7unr1iN3yjOr/eniUwWBwJikplri4MLt1166dy9X+C1puf+ZS/ERFRbF6dWqd8CFDhqRb//DDD+Pj40NYWBhr164t6PAKhBK+IiIiIiIiIkVAUlL8Tb/S3uKeEVdXLwAiIg6mm52bE5UqdQTgzz9HceHCZuLirnDgwHz27v0CgIoVO6Tb57VrZ0lMjLXNILX2ceTIQvbs+ZTY2FAuX97JmjXPABAUdK+tdquPTzBGow+Jidf4449niI4+S3T0WVvbjPj7V6ds2Wa21zVq9M7WsTk7u1K37hMArFnzFHv2fMq1a+fw86t6/cFgqa5ePUJk5Mls9ZlWcHBnAPbs+Yi9e+cQG3uZq1ePs2zZABYubE9CQjQAvr6VAdi3by6hobtJSLjGtWvn2bFjWrb3FRiYWh/5wIFvOHToB+LjIzh7dj3//DMlw+MOCKgNwN9/v2J7SN/Bg99y9erRHB+nI+T2Zy7Fz86dO0lISMDNzY2GDRumW+/q6krjxo0B2LJlS476Xrt2LQ8//DD33nsvvXr1YsqUKVy8mHXd8oKmGr4iIiIiIiIiRYDFkszMme6Zrk9bKzYjJUs2ICxsH8uXP8r27e8xYMCuXMXRtOnrHDu2mPDwg3z3XXO7deXLt6JatR6216VKNcRgcCY0dDezZnnSrdsiqlV7kAoVWlOt2kMcPfoza9Y8zZo1T9u2cXHx4J573rK9dnZ2pVGjF9m06XX27ZvLvn1zgdSHuBkMThmWKIDUGZ8XLmwGcnZrf/PmE7l4cTNnz/55PbaM2337bSMeemgVpUunTyhlpmbN3uzd+xlnz/7JqlVPpFt/7NgSatXqT82afdiyZRKXL2/nm2/uynb/aVWv/hD+/jWJiDjE0qX/JT+9vSsSHX06XfsmTcaxdGlf9u79nAMHvsHFxQ2zORJnZ+MtXSAoSLn9mUvxcuRI6qz2ihUr4uKSceqzSpUqrFmzxtY2u9avX2/3+qeffiIkJIQPP/yQQYMG5Sre/KAZviIipNaqMpvNherLytFxZPRVVGt7iYiIiNzOWrSYRPnyrbJ8YFhWfH2D6dNnMzVqPIKbWwBOTi74+FTm7rtfpkeP5XYPM/Pzq8K9987G07Ncun66dv2O5s0n4udXHScnV0wmf6pU6U6fPhspWbK+XdsmTcbRtOnreHlVwNnZjfLlW9GjxzKcnFyBjOsGly/fEoASJWpRsmS9bB+fi4uJnj1X06bNdEqVaoirqxcmky9ly7agY8e5jBploV27mXh5lcffv0a2+7XG2aPHcpo2fR1//xo4OxsxmfwICmrPgw8upVat/gB4ewfRq9daKle+Hw+PMri4uFOmTBM6dpybw30to0qV7tePwZ/69YfRsuXkDNvXrNmHe+/9EF/fqkAKHh6l6dr1O8qWbZ5h+8Iotz9zKfzMZjNRUVF2X2k/N6cVEREBgL+/f6b9WddZ22albNmyjBs3jm3bthEWFkZsbCwbNmygS5cuxMXF8fjjj/Prr7/m8Kjyj8GiT+35JioqCl9fX3bu3Im3t3eu+jCbzbzwwgsAvBYahjGL9iIZSQAmlQwAYOrUqZhMt/YHXnGU9ndNslbcxlF0dDQNGjQgMjISHx8fR4dTKOXFOQ10XpNbp3Na0ZT2d3/atGkYjfrtz086r2XP6NGOjkBu1ZUr+/j667o4ObkwfPg1u5q6ADt3fsC6dc/RvPkbNGv2Wp7vPzk5EWdn1zzvV3Ivv3/m+WVa9it13JxT8Z3XGfL660ycONFu2YQJEwgJCUnXdtKkSbz++uu0atUq3Yxcq9dff51JkybRvn17W73f3LBYLPTs2ZNFixZRtWpVjhw5YitH40jFdySIiIiIiIiISLFw9OgivvqqLidPLsdsjuTixa2sWDEQgAoV2tole5OTE7h48R+2b58K5N+t/Ur2Fh4F9TMXxxk7diyRkZF2X2PHjs2wrZubGwAJCZmXIrHODnZ3z7xMTnYYDAYmT06dNX/s2DH27NmTxRYFQzV8i5AEgwE0ITsdC5B4/XtXwPHXUQqfhEJwdamwMxqNTJ061dFh2FgsFhITEwkODub8+fOODicdzcwSERERkYJ06NB3hIXtY9GiLnbLXVw8aNnyXbtlW7e+zebNqTMBK1bsgL9/9QKLUxxDP/Piz2QyZfvOruyUa8hO2YfsqlGjBiVKlCA8PJyjR49Sv379rDfKZ0r4FiHvBpZwdAgixZbBYCh0twW7u7vj5+dHeHi4auZKsaQLmRnThcyb00VMEZHbU5cu8ylZ8i72759HVNRJjEYfypdvQ/PmIQQG1r2htQGj0Zvy5VvRocNnDolXCpp+5vKf6tVTE/6nT58mKSkpwwe3HT9+3K7trXJ1TZ3xn5SUlCf93SolfEVERMQhdCFTREREssvJyYUmTcbSpEnGt3Cn1bz5BJo3n1AAUUlhoZ/5dRkkNm9HDRo0wNXVlfj4eHbs2EGTJk3s1icmJrJt2zYAmjZtesv7u3LlCpcvXwagQoUKt9xfXtBIKOQK223mhZHZbGbcuHEAvPPOO7rVPAt6f0RERERERESkuPLx8aFDhw4sW7aMOXPmpEv4/vjjj0RFRREQEEDbtm1veX/Tpk3DYrHg6+tL48aNb7m/vKCEbyFXGG8zL8xMJpMSmiIihZguZGZNFzKzT++NiIiIiGTk1VdfZfny5Xz++ee0bduWvn37ArB7925Gjx4NwMsvv2z39+SMGTOYMWMGzZo147vvvrMt37dvH7Nnz2b48OHUqVPHtjw+Pp5p06bx7rupdcTHjBlTaP4+VcJXRERECowuZOaMLmSKiIgUbklJcWze/AaHD/9IdPRpXF09KFu2Oc2ahVC2bNa3isfFhfH332M5fnwJ8fHheHmVp1q1HjRvPhGj0duurcWSwv79X7J794dERBzGxcWdwMB6NGv2OuXLt8yvQxQpku655x4mTZrE+PHj6devH+PHj8fLy4u9e/eSkpLC/fffzwsvvGC3zdWrVzl16hTBwcF2yxMTE/noo4/46KOPKFmyJBUrVgTgwIEDxMbGAjBkyBBeeeWVAjm27FDCV0REREREREQkF5Ys6cGpUytsr83mSE6eXM6ZM3/Qu/dGSpdulOm2SUlmfvqpPaGhu23LoqJOsmPHdC5c2ETv3hswGJxs61avfoq9e+fYXickRHH69CpOn15Fly7zueOOfnl8dFLkqIavnVdffZX69eszffp0tm/fzsWLF7nzzjsZPHgwI0aMwNnZOVv9BAcHM2nSJDZu3MjBgwc5dOgQCQkJlCpViq5du/LEE0/QqVOnfD6anNFIEBERERERERHJoXPnNnDq1AqcnFzo1OlLKlXqREzMRVavfpILFzaxdetbdOv2c6bb79z5PqGhu/HyqkCXLvMJDKzLqVOrWb58ABcubObkyeVUrtwVgKtXj9mSvc2bT6RevaEkJETx558vcPz4Ev7+e6wSviIZeOCBB3jggQey1TYkJISQkJB0y/38/Bg/fnweR5a/nLJuIiIiIiIiIiJSuERGnmT6dAMLF7YnLi6MdeueZ86cYGbMcGH9+pfyff8XL24BIDi4K3fc0Q939wACA+vQsuVkAC5c2HLT7Y8d+wVITeBWqNAaN7cS1Kz5CMHBnQEIDd1jaxsWtg+AEiVq06zZ63h4lMLPrxr33fcZANHRpzGbo/L0+ESk6NIMXxEREREREREpckJDdwHg4uLG11/Xw2JJoXz5VgQE1KFMmazr596qhIRoALy9g+yWe3tXvL7+5gnYBx/8DYslBZPJ74Z+U7czGr1sy4xGn+vfWezaWiypr52cXHF19chR/CJSfCnhKyIiIiIiIiJFjjXhe+LEUurVe4Y2babj4nLzh8MmJydisSRnuM7Z2WhXMzdrllta7+ZWIt2yq1ePceHCJgAqVuxgW16+fEv8/WsQHn6ArVvfoX79YSQkRLNu3UgAatd+DCcnpXhEJJX+NxARERERERGRIsf6sLPy5Vtx772zspWsXb36Sfbv/zLDdb16rSUoqG1ehpgjFksKa9YMJTk5gapV/48SJe6wrXNycuHBB3/np586sGHDODZsGGdbV7lyV9q2/cARIUtho4e2yXUaCSIiIiIixYjFYiEhIcHRYdgxm81231tvQS4MjEYjBoPB0WGISC5YZ/i2bv1eDmfmFk5btrzF6dOrcXcP5N57P0y3/vjx34iKOp1u+fnzGwkL20uZMk0KIkwRKQKU8BURERERKUYSEhJ44YUXHB1GpsaOHevoEOxMnToVk+nmt4CLSOFjNkcSFXUSL68KlC7dONvbdejwGe3bf5zhOmdnIwApKcmkpCRm2MbJySVfSiecObOWzZsnAgY6dpyHl1c5u/UREYdZv/5FAFq3nkrNmn1JSoph48bXOXRoAcuXP8rAgQd1AUtEACV8RURERERERKSIsZZzqFChdY6SnNkp6XDgwNesXDk4wzbNmk2gefOQHMd7MzExF1m6tB8WSzING46mSpX707U5fPgHLJZkatToTaNGo23LO3X6knPn1hMRcZgLFzZRrlyLPI1NRIomJXxFRERERIoRo9HI1KlTHR2GHYvFQmJiIsHBwZw/f97R4dgxGo2ODkFEcsFazqFkybscGEVWieasE9EWSwrLlvUjNvYipUvfTcuWkzNsFxV1CoDAwDvtljs7u+LvfwfXrp0jMvKEEr63O9Xwles0EkREREREihGDwVAoSxS4u7vj5+dHeHh4oarhKyJF038J3wY52q5Tp3l06jTvpm3q1BlEnTqDsuzLaPQCIDr6jN3y6OjT19d7Z9nHpk0TOHNmLUajD127foezs2uG7dzcSgAQFrbPbnlyciIREYcAcHcPyHJ/InJ7KPpVzUVERERERETktmIt6VCq1F0Oi6FMmWYAnDy5lIMHvyUuLowrV/bx99+vXF/f1K59cnICSUnxpKQkA3Dq1Eq2bn0bgLZt38fLqzxJSfG2r+Tk/+oIV6rUCYDDh79nx44ZxMRc5OrVY6xcOYhr187i6upJuXL35Psxi0jRoBm+IiIiIiIiIlJkpKQkERa2Dy+v8ri7BzosjvLl76FSpU6cOrWCZcv6261zdjbStOl4u2U//9yRs2f/pGPHudSpM4ht2yZjsaQAsHLl4HR1g2vXHmibjVyx4r3ccUc/Dh78lj//HMWff46ya9umzfRszSgWkduDEr4iIiIiIiIiUmSEhx8kOdns4Pq9qbp3X8SmTRM5cuRHoqPP4OLiTtmyzWnePITSpRvddFtrsje7Onf+mjJlmrFv3xdERBzG2dlIqVKNaNz4ZSpV6ngrhyHFhWr4ynUaCZIjFouFhIQER4dhx2w2231fmGrCGY3GHD0xVkRECpbOazmj85qIiBQGgYF1GTWqcJwfXVzcadVqMq1aZfywtbQefnjdTV9nxWBwokGDZ2nQ4NkcbScitx8lfCVHEhISeOGFFxwdRqbGjh3r6BDsTJ06tVA+NEVERFLpvJYzOq+JiIiIiBR+emibiIiIiIiIiIiISDGhGb6SI0ajkalTpzo6DDsWi4XExESCg4M5f/68o8OxYzQaHR2CiIjchM5rOaPzmoiIiEghphq+cp1GguSIwWAolLdyuru74+fnR3h4eKGqdSgiIoWbzmsiIiIiIlLcqKSDiIiIiIiIiIiISDGhhK+IiIiIiIiIiIhIMaGEbyZOnDjBI488QkBAAEajkWrVqjFx4kSSkpIcHZqIiEiO6JwmIiIikrFNm0KYPt3AH3+McHQo2ZaUFMfff4/liy+q8f77Rj780I9Fi7pw4cKWLLdNTIzhjz9GMH26E9OnG7h48R+79fHxEaxdO5I5c6owc6YnX311J1u3TiY5OSG/DkdE8oFq+GYgLi6ODh06cPz4cTw8PChdujQnTpwgJCSEuLg4Jk+e7OgQRUREskXnNBEREZHiZcmSHpw6tcL22myO5OTJ5Zw58we9e2+kdOlGmW771Vd3EhV1IsN1SUlxLFzYjtDQ3bZlYWF72bBhLJcubaVbt5/z7iAkf+ihbXKdZvhmYP78+Rw/fpy2bdty+fJlzpw5w7Zt2zCZTEybNo3Y2FhHhygiIpItOqeJiIiIFB/nzm3g1KkVODm50KXLfIYOvcKjj+6lbNnmJCcnsHXrWzfdPirqBNWq9cDdvWS6dTt3fkBo6G58favSp89mnnkmjI4d52IwOHH06CLCww/l12GJSB5TwjcDe/fuBWDw4MF4enoC0LBhQ1q0aEFiYiKHDx92ZHgiIiLZpnOaiIiISPFx8WJq2Ybg4K7ccUc/3N0DCAysQ8uWqXdtZVXW4cEHl9Kt28+4unqkWxcbe4mgoPa0b/8hZcs2xc2tBHXqDKJkyQYAREQo4StSVGiudwauXr0KgI+Pj91y62vrehERkcJO5zQREREpaPv2zWPlysFUrnw/DRqM5K+/xhAefgAfn2CaNh1PrVoDctRfSkoSn31WntjYy/Ttu4UyZZrY1iUnJ/Lpp2WIjw+nb9+tlCnTGIDTp9fwzz//4+LFrSQlxeDrW4XatQfRqNGLODk5Z7nPTZtC2Lx5IvXrD+fee2fZlkdGnuSLLyrj6urJiBHX7LaJiDjMxo2vcfr0GhITo/Hzq0HDhqOoW/fxHB3vzSQkRAPg7R1kt9zbu+L19VE33b5y5S6ZrmvTZlqGyy2WZABMJv9sxykijqWEbx4ym82YzWbb68jISABiYmIwGAyOCuu2YDAYiIqK4tq1a1gsFkeHI5InNK4LTkxMDIDe5zR0TnMs/f5LcaRxXXB0XpPC4tKlbfzyy/2kpKQ+KDYi4hDLlz+Kl1d5goLaZbsfJycXatToza5dMzl8+Ae7hO+ZM2uIjw/H37+GLdl78uQKFi3qbNdHePhB/v77FSIiDtGx4xd5cHT2IiKOsGBBU8zmq7ZlYWF7WbVqCImJ12jQYORNt7dYUjJ9MJrB4Iyzs6u1ZRaR5M3vfXJyIklJcRw9uojQ0N34+9ekXLnmedK35CPV8JXrNBLy0DvvvMPEiRPTLW/ZsqUDohERkZyKjo7G19fX0WEUCjqniYgUfTqviaPFxl6mZcvJ1Kr1KHFxoaxcOYTLl7fzzz9TcpTwBahV69HrCd8fadXqf7YL0IcP/2BbbxUc3Inu3RdjNkdSoUIbXFzcOXhwPn/+OYp9++bRpMl4/Pyq5N2BAhs2jMNsvkqpUg1o3/5jfH2rcvr0KlasGMSGDa9Sp87jGI1emW5/9ux6Fi7M+D2pXXsgnTrNy9N4s7J69ZPs3/8lABUrduC++z7HyUkpJJGiQr+teWjs2LGMHj3a9jolJYXw8HACAgI0GyqfRUVFERQUxJkzZ9LdtixSVGlcFxyLxUJ0dDTlypVzdCiFhs5pjqXffymONK4Ljs5rUlh4eVWgceMx178vR5s20/nxx9ZcufJvjvsqU6Yx/v41iYg4xMWLWyhbthnJyYkcO7YYMHDHHf3t2let2h1InalqsSRTr95QDh/+gQsXNnH58o48TfimpCRz8uQyAO67bw4lStS6HsOD1Ko1gL1753DmzB+2mIqa6OjTREYex8enkqNDEZFsUsI3D5lMJkwmk90yPz8/xwRzm/Lx8dEHCCl2NK4LhmZA2dM5rXDQ778URxrXBUPnNSkMSpasb/fa378GADExF+yWJyXFZ9KDAReX//4eueOO/mza9DqHD/9A2bLNbOUcypW7B1/fyrZ28fFX2bBhHMeO/ZJuXwCJiTG5PKKMxcWF2vqcP79hhm2uXj1y0z4qVGjNs8/GZbjOYMi65nBe69RpHu3bf8KJE7+xdGlffvnlAR5//DienqULPBYRyTklfEVEREREREQk31kflmaxpNgtnznTPcP2Pj6VGDLkpO11rVoD2LRpAkeOLKR166kZlnMA+OOPYRw6tOAmkeRtfevME9b/SUi4dtP1ha2kA4CLi4nq1XtSvXovDh1awOHD32dZi1gcTDV85TqNhAxYZzBFRdk/3dL62t9fT6YUEZGiQec0ERERcRz7xOp/id7clYfy9a1MuXItOH9+A+fO/cWxY4txdjZSo8YjtjYpKckcObIQg8GJ//u/36hUqaMt0bxixSBbXdqs/DerNuvksLOzEQAPj1I8/fSlnB1UjmX13uV96S0/v6oAREWdzPO+RSR/KOGbgTp16gAwd+5cevbsiaenJzt27GDjxo24urpSvXp1B0coNzKZTEyYMCHd7cciRZnGteQFndOKJv3+S3GkcS1y+wkN3W33OiIitayBl5d9felRo7I/4/aOOwZw/vwG1q59lvj4cKpWfRA3t/8uYMfHh5GSkoi7eyCVK3fJdexeXuUBiIo6bbf82rVz6dq6uwfi7GwiNvYykZEn7MpLpKQkZethZ0FBbbP1Plgf/BYdfcZueXT06evrvbPsIyPJyQnMnVuN5OQE+vTZjK9vcJq+U/fl4pLxTGwRKXycHB1AYTRgwACCg4NZt24dpUqVomLFijRu3Biz2czo0aPx8PBwdIhyA5PJREhIiD5ASLGicS15Qee0okm//1IcaVyL3H6uXTvHli1vce3aBa5c+Zc//xwFQGDgnbnus2bNR3B2NnLlyh4gfTkHd/eSuLp6Ehd3hZ07PyA2NhSzOYqLF7dx7NiSdP25uqYmUKOiTpCcnGBbXrLkXQCcOrWcU6dWAZCYGMtff72Urg9nZyMVK94HwO+/P8L585uIj7/KuXN/M39+I3bsmJHr471RmTLNADh5cikHD35LXFwYV67s4++/X7m+vqmtrcWSQlJSPElJ5iz7dXY2UqJEbWJjL7Fq1eNcubKX+PhwDh5cYCuPkbZvESncNMM3A+7u7qxatYoxY8awdu1aLly4QHBwMI899hivvvqqo8MTERHJNp3TRERExFE8PcuxadPrbNw43m5548bjct2nm1sJgoO7cuzYL5hMflSufL/deoPBQN26T7Bz5/usW/cc69Y9d9P+ypRpAsCJE0v54AMT/fvvpFSpuyhduiGVKnXi1KkV/PxzR9zcSpCYeI3MSia0ajWF8+f/4tKlf/j++xZ263bsmE7dukNyPfs2rfLl77HFtWxZf7t1zs5Gmjb977221gW+sRZyZlq3/h/ff7+RM2fW8vXX9kn5smWbpXuvpRBSDV+5TjN8M1GtWjV++uknwsPDSUxM5NixY0yYMAEX/fKIiEgRo3OaiIiIOEKpUg24//4f8PevgbOzkYCAOnTt+j0VKrS6pX6ts3qrV38YF5f0dw20bDmZe+55ixIlauHi4oGHRxnuumsENWv2Sde2QoXWNG36Ou7ugenWde36LbVqPYbJ5E9yspmgoPb06LE8w5gCAmrRt+827rijPx4epXFycsXXtwqNG7/CgAE78yTZa9W9+yLuvnsMvr5VcHJyxWj0oVKlTjz88J+ULt0o1/0GBt5Jv37/UKNGb9zdS+Lk5IqPTyUaNhzNQw+ttNVCFpHCz2CxWPL28ZQiIiIiIiIihdzo0Y6OoPjat28eK1cOpnLl+3nwwd8cHY5IoTdtWh51VLt2HnVUCO3f7+gIihTN8BUREREREREREREpJpTwlUJv3bp1GAwG6tevT1JSUoZtDAYDfn5+BRuYSB6zWCzMnTuX1q1b4+fnh9FoJCgoiAEDBvDXX3/Z2oWEhGAwGJgxY4bjghWRXNE5TW4XOqeJiIiIOI4SvlJk7Nmzh+nTpzs6DJF8M2jQIB5//HH++usvUlJSqFChAqGhocyfP5/WrVvzxRdfABAYGEjNmjUpUaKEgyMWkdzSOU2KO53TREREHMDFpfh+SY6ohq8UeuvWraNdu3YAeHp6sn//fipWrGjXxmAw4Ovry9WrVx0Qocit27RpEy1atMDLy4sFCxbwwAMPABAXF8f333/Pl19+yW+//Yanp6eDIxWRW6FzmtwOdE6TokI1fEWksMizGr716uVRR4XQnj2OjqBI0QxfKVJiYmJ49tlnHR2GSJ7bsGEDAEOHDrV9MAZwd3dn0KBBrF27Vh+MRYoZndOkuNI5TURERMSxlPCVIuOBBx6gYcOGLFmyhMWLF2fZftGiRTRt2hR3d3e8vLxo164df/75ZwFEKpK/VO9QpOjTOU0klc5pIiIiInlPCV8pMpydnfn0009xdnbm2WefJSYmJtO2CxYs4KGHHmLr1q14eXnh4uLCunXr6NChg92DQkQKi8aNGwMwa9YspkyZwqlTpxwckYjkJ53TpDjTOU1ERMRBHF1nVzV8Cw0lfKVIadSoEc8++yxnzpxhwoQJmbZ7/fXXAZg+fTqhoaGEh4czatQokpKSeOONNwoqXJFsa9OmDQMHDiQ+Pp4xY8YQHBxM+fLl6d27N/PnzycxMdHRIYpIHtM5TYorndNEREREHEsJXylyJk2aRFBQEO+//z57MijaHR4eztGjRylZsiTPP/88AE5OTrYPxVu3bi3IcEWybd68eaxfv56hQ4fSqFEjrly5wg8//MCAAQOoXbs2Z86ccXSIIpLHdE6T4krnNBERERHHUcJXihwvLy9mzZpFUlISQ4cOxWKx2K2Pjo4GoFy5cum28/X1JSoqqsBiFcmpVq1a8dFHH/HPP/8QGRnJ6tWradasGUePHuXll192dHgiksd0TpPiTOc0EREREcdQEQwpkrp3706PHj1YtGgRn376qaPDEckXbm5utG/fnpo1axIUFMTff//t6JBEJB/onCa3A53TRERECoBq3cp1muErRdbMmTPx9vZm7Nixdst9fHwAOH/+vN1MqWvXrhEZGWlbL1KYWCwW4uPjM1xnMBhsbUSkeNI5TYoTndNEREREHEsJXymyypcvz1tvvUVERITdcn9/f6pXr05oaCjvv/8+ACkpKbaH3jRp0qTAYxXJymuvvUbDhg355ZdfMJvNtuWnTp3iiSeeAKBly5aOCk9E8pnOaVKc6JwmIiIi4lhK+EqRNnz4cBo3bpxu+cSJEwEYNWoUJUuWJCAggOnTp+Ps7Mxrr71W0GGK3FRiYiK7d+/mwIED9OjRA19fXypXrkzp0qWpXLkyy5cvp1SpUrz11luODlVE8pHOaVIc6JwmIiIi4nhK+EqR5uTkxKefforLDXVq+vbty08//UTjxo25du0aCQkJtG7dmlWrVtG6dWsHRSuSMVdXV3799Vd+//13+vTpQ7ly5bh06RKRkZHUrFmT559/nt27d1O1alVHhyoi+UjnNCkOdE4TERERcTyDRQW0RERERERE5DYzerSjIxARSTVtWh511KJFHnVUCG3c6OgIihTN8BUREREREREREREpJpTwFRERERERERERESkmlPAVERERERERERERKSZcsm4iIiIiIiIiIiIihZqL0nySSjN8RURERERERERERIoJJXxFREREREREREREigklfEVERERERERERESKCRX3EBEREZFi6/z585QrV87RYUgBu3jxIqVKlcLJSfNbRETkNqIavnKd/gKSYslgMGT4ZTQaqVKlCs8//zzh4eGODlMyERISgsFgoHPnzo4ORUREirBDhw5xxx13MGfOHEeHIgXo+PHjNG/enLfeesvRoYiIiIg4hBK+cltJTEzkxIkTvP/++7Rs2ZJr1645OqRiY8aMGYSEhLB3715HhyIikiesFws3b958S23EMcxmM3369CE6Oprvv//etnzZsmWULl060wuLJ06coF+/fpQsWRKTyUStWrWYMmUKycnJGe5ny5YtdOvWjRIlSmAymahRowYTJkwgPj4+w/ZhYWE8++yzVKpUCaPRSGBgID169GD37t23fMwnT560jcmLFy/ecn9F1ebNmzl58iQTJ05kw4YNjg5HREREpMAp4SvF2vTp09m0aZPta/HixTzxxBMAHDhwgNmzZzs4wuJjxowZTJw4UQlfEREpFD7++GN27dpFiRIlmDdvHgBvvPEGXbt25fLlyxluc+bMGZo1a8aCBQu4cuUKCQkJHDx4kDFjxvDkk0+ma798+XJatmzJb7/9RkREBAkJCRw5coQ33niDjh07kpiYaNf+2rVrtGzZklmzZnH69GkSExMJCwvjl19+oUWLFuzatSuv34bbUr9+/ejduzfJycmMGDHC0eGIiIiIFDglfKVYa9asmd1X9+7d+eyzz+jduzcAK1eudHCEIiIikteSkpKYNm0aAJMmTbLV8P3jjz8wGAxUrFgxw+1ee+01Ll++jI+PD7Nnz2b58uX0798fgLlz57JlyxZbW4vFwrBhw0hKSqJChQrMnTuXlStXMnToUAD++usv5s6da9f/rFmzOHjwIEajkUmTJrF69WrmzJlD2bJliY2NZcyYMXn+XtyuZs2ahbu7O7t27dLfeyIicvtwcSm+X5IjSvjKbalVq1ZA6m2VIiIiUrwsXLiQ06dPExAQwKBBg2zLq1SpwurVqxk8eHC6bSwWC4sXLwbg3XffZdiwYXTq1Imvv/6aevXqAfDLL7/Y2h89epQTJ04AMH/+fAYNGsR9993HRx99ZCsVcWOicdmyZQA8+eSTjB8/nvbt2/P444/zwQcfALBu3ToSEhLy5k24zQUGBjJw4EAAW/JfRERE5HahhK/cls6cOQOkfvC70fLly+ncuTMBAQG4ublxxx13EBISgtlsTtc2JiaGV199lZo1a+Lm5oanpyd169blpZde4tSpU+naJycnM336dOrVq4e7uzsBAQH07t07w7aDBg3CYDAwdOhQ/v77b1q0aIGHhwfBwcFMnjwZi8UCwOzZs6lSpQru7u40adKEVatWZXjMud33pk2buOeee3B3d6dixYqMHz+epKQkW9urV69iMBhs/fTt2xeDwcDVq1czjONGH3/8se39q1evHj///HOmbYODgzEYDHz33Xd2yy9evGirWXjy5Enb8rQPf9u3bx8dO3bEy8uLcuXK8dJLL9k+VP/www/UqVMHNzc36taty7fffptu3/PmzcNgMHDHHXdw9uxZevXqhY+PD4GBgTz55JNERkYCsGbNGho3boy7uzvVq1dnxowZdv388ssvGAwGSpUqla4eZEpKiq2u5M3eBxERubnff/8dSD2feXh42JbPmTOHe++9N8Ntrly5Yjt3tW3b1rbcYDDYXh85csS2PO15rmnTpnZ9WV/feC48f/48AHfffbfdcuvrhIQErly5cpMjk5wYNmwYAGvXriUuLs7B0YiIiIgUHCV85bZy7do1Fi1axIcffghgu+3S6r333qNLly6sWLGC8PBwzGYzhw4dYuLEiXTv3t2WZLXq3r07b7/9NocPH8ZsNhMbG8u+fft47733qFWrVrqZPf3792f06NH8+++/xMfHEx4ezg8//ECLFi24cOFChjHv3LmTDh06sGnTJuLi4jh16hRjx47lzTff5M0332TEiBGcOHGC+Ph4tm3bRteuXTN8QElu9r1r1y7uvfdeNm7cSHx8PGfOnOGtt95i5MiR2X7Pb+bjjz/mmWeesb1///77L7169eKHH37Ik/6tzp07R+vWrVm1ahUxMTFcuHCB9957j2eeeYZvvvmG3r17s3//fsxmM/v27aN///52D/hJ69q1a7Rp04affvqJ6OhowsLC+Pzzz+nZsyd//PEHXbp04Z9//iE+Pp6jR48yatQopkyZYtu+S5cu+Pr6Ehoayp9//mnX9+bNm7l8+TIeHh4ZPkhIRIqW33//nbZt2+Lj44O7uzv16tVjxowZpKSk2LW72YO2vvvuOwwGA8HBwXbLLRYLn3/+OU2bNsXLywuTyURwcDADBw7M9AFyaePx8vKidevW6f4fysrVq1cZM2YMNWrUwM3NjRIlSvDAAw/YlTpI699//6Vjx454enpSqlQpRo8ezcGDBzM93pxcnLyZdevWAaRL7hoMhky3STuz1tXV1W6d9XXaNpUqVbL1lzYRnPZ1pUqV7JZba/q6ubnZLU/7OrszfDN6bzN7UFzaC6A36ty5MwaDgZCQENuytGPy9OnTvPjii5QpUwZPT086d+7MgQMHADh+/Djdu3fH29ubMmXKMGLEiAwfiGvta9OmTUyePJmKFSvi7u7OPffcw6ZNmwAIDQ3l0Ucfxc/PjxIlSjBgwAC7WstJSUmULFkSg8HAr7/+mm4fL774IgaDge7du9uW3XnnnZQsWZKEhAQ2btyYrfdVREREpDhQwleKtebNm9s+ZBgMBry9vXnooYdwd3fns88+o2PHjnbt+/TpQ7du3Xjsscf48ccfWblyJU8//TSQelum9VZMgG3btvHHH38AMHLkSFatWsXKlSuZOHEiFStWpEqVKrRo0cLW/rfffrMlEfv168dvv/3GggULqF69OufPn2fChAkZHsPWrVvp1asXy5Yt47vvvqNmzZpA6oe31157jQkTJrB69Wree+89vLy8SEpK4o033rDrI7f73rJlCz179mTZsmX8+OOPNGzYEIBPPvmEs2fPAuDt7W37sAbw3HPPsWnTJry9vW/2oyElJcUWZ4UKFfjiiy9Yu3Ytw4YNs32QzCt79+7l7rvv5tdff+WXX36hWbNmQGo9xkcffdT28/vkk08oVaoUAK+//nqGfZ07d44SJUqwcOFCfv/9dx544AEgdWZv+/bt6dWrF8uXL+frr7+2zSB/++23bR/CTSYTPXr0AODHH3+069t6K3GXLl3sZqSJSNHzySef8MADD/Dnn38SHR1NfHw8//77L6NGjbIrMZBbkyZN4sknn2Tr1q3ExMSQkJDAqVOn+Oqrr2jevDmTJk2ya//RRx/ZxRMTE8Nff/1Fhw4dbOeyrFy5coWGDRsyZcoUjhw5gtlsJiIigt9//51WrVqlSx6fP3+eNm3asGrVKmJjYwkNDWX69On06tUr033k5uLkjU6fPs3Zs2dxcnKiZcuW2doGICAgAGdnZyD1Ymta//zzDwBly5a1LStVqpTtWJ588kkOHDhAdHQ0c+fO5fvvv8fJySnDB73lhdy8t7n16KOPMnXqVC5dukRsbCwrVqygbdu27N+/n1atWvHrr79y7do1Ll26xOzZs3nkkUcy7euVV15h7NixnDlzhvj4eDZu3EiHDh1sF5i/+eYbIiMjiYiIYP78+XYPvnNxcbEd343nT4AlS5YA0LNnT7vlrVu3BlDCV0RERG4rqnost6UmTZrYkpdpVahQwfaBweq+++7j4MGD/Pnnn6xbt46uXbsC/83eufPOO3n//fft2o8ZM4aIiAi8vLxsy61lCLp06cL8+fNtyxs0aECtWrX47rvvmD17drpZRYGBgXz99de2WUR33nknderUISUlhfbt29tm5LRv3x5XV1eee+451q9fT0pKCk5OTnm671atWlG5cmXi4uJYv349/fr1w9nZmWbNmlGpUiVOnTple0BeVo4ePWr78P7111/bbpdt27YtmzdvZvv27Vn2kROLFy+2zaC65557qFSpErGxsVStWtXu51emTBn+7//+j8OHD3P+/Hnbg37S+v33322J4Q4dOlC1alXOnj2Lq6sr33zzje19r1evHvXr1ycyMpIdO3bYLgD07duXefPm8fPPPzNr1ixbgiGzD6si4lj//PMPLjl4UMTVq1d54YUXgNT/s8ePH4+XlxdffPEFP/30E19//TUDBw6kffv2uYonOTmZqVOnAtCxY0eGDx+Or68v+/bt48svv2Tfvn08+OCDtvahoaGMHj0aSC0d8NJLL+Hl5cXMmTNZvnw5zzzzjG3W7c0EBgby2muvMXv2bAYMGEDdunU5f/48r732GqdPn+all15i69attvYffPABERERODk58cILL9CpUyfOnj2bacL7xouT/fr1Izo6mtdff50jR44wYcIEPv300yzfH+u5pVSpUvj4+GTZ3srNzY1mzZqxYcMGxo0bR5UqVahWrRpz5861JbObNGlit81nn31GWFgYf/zxB7Vr17Ytd3Jy4osvvsjW+TA3cvre3or169fz6aefUrlyZZYvX860adO4fPkyderUoWHDhsyePRtnZ2c++OADVq9ezbJly9i0aRPNmzfPsK9p06ZRr149Nm/ezBtvvEFsbCwNGjSgSpUqfPvtt/j7+/Pll1/y3XffsXv3bhYuXEjfvn2B1AvzH3/8MUuWLCEhIQGj0QjAgQMHOHLkCK6urnYzfAGqVasGkO0LBiIiIkWaHm4m12kkSLE2ffp0uw9bV69eZfny5cycOZO//vqLTZs2UatWrXTbXbx4kTNnzthqrJYuXRrArq6er68vAKdOneLrr7+mRYsWlC5d2nZrbZkyZez63Lt3LwCVK1dOd7ttuXLlOHfuHIcOHaJu3bp26+6//367D+G1a9cmICCAsLAwHnroIbu27dq1AyA+Pp6rV69SokSJPN136dKlqVWrFjt27Eh3G25OWW/TdHJy4p577rFb16lTpzxN+LZp08budtnAwEBq1arF9u3bM30PIXUc3JjwrVSpki3ZC2A0GmnatClnz57lwQcftCV7ITXhW6JECcLDw+3er/bt21OyZEkuX77M+vXradeuHYcPH+bgwYOYTCbbrGERKRyeffbZHLW3lo9xc3NjxYoVtlmhXbp0oWHDhuzatYtFixblOuF76dIloqKiAFiwYIHt//o2bdowbNgwTp06ZVdKYPHixcTHx1OyZEnWrl1ruxjZoUMH7rjjDg4fPszWrVvT1aHNyODBg9M98KxixYq0a9eOf/75h2vXrtn6X79+PZA6QzRtaZvo6OgM39PcXpy8kfVcHRAQkOXx3OiVV16hW7duHDlyhEaNGtmtc3FxsUukQ+ps4v3796frJyUlhVWrVvHYY49lmUjPjZy+t7fi448/ts1U7tChA6dPn7bNsP3ll18ICgoCUi94V61alfPnz7N27doME74vvPACo0aNAlLPhZGRkfzvf/8DsP0tBal/Bxw5coTt27ezdu1aW8K3VatWlC9fnnPnzrFy5Urb+dJ6wbRdu3b4+/vb7dM6DlQbWURERG4nKukgxZp1tqn1q3PnzsyYMYOnn36ayMhI3nzzTbv2GzZsoHbt2pQtW5YmTZrQvHlzmjdvbqspm7buYtu2bSlfvjxRUVE89thjVKtWDW9vbzw9PbnvvvtYunSpXd/WD+cffvihrV/r17lz54CMZ5/cWOcPsH2Ytn7It0pbRiFtDcC83Ld1H7f6FHHrg99cXV0zrZWYV/LiPcxNX2n7S9uXs7MzDz/8MPDfbanWcg733XdfluUwRKRwO378OJA6uzdtCQCDwUCHDh3s2uSGt7e3LYk4c+ZM/v33XyIiImzrb6wba73oV7JkSfbu3cvmzZvZvHkzO3bssF3o27ZtW7b3Hx8fz4EDB2z9WGOxWCyEh4fb2lkv7Flvqbfq1KlThv3eeHHS+hUREUG5cuWIjo7m0KFDWcZnfVCan59fto/J6oEHHmDWrFl4enqmW9exY0e7JLLFYqFfv35cvHiRqlWrMnfuXFauXMlLL72Ei4sL8+fP55NPPslxDNmR0/f2VtzYp3WfJUuWtCV74b8Z0kCmF4VvvKCZNv60s6cNBgNt2rRJ15eTk5OtZETasg7Wc2hGd8hYx0F2HyYrIiIiUhwo4Su3JWtZButDXSB1VkyvXr2yXT/W09OTDRs2MHr0aBo0aGCr/RcbG8vq1avp1q2breYfkO6BbxmJjY3N2YFkkyP3LRnr06cPAD///DMpKSkq5yBSiG3atAmLxZLhV0bi4uKAjC8CWROG1ja54e3tbbttPSQkxHY3gdFopEGDBnzwwQd2FyitF/3279+f7qKf9eFX2bndPSkpiZEjR+Lt7U3t2rVtfVhL8oD9hVHrhb0ba5JndlEvtxcnb2S9CJfRw8OyY/jw4Vy6dIldu3Zx5MgRW/L3xgeWrlu3jr179+Lm5sbq1asZNGgQ9913H1OmTGHs2LEAdiWD8lJO39tbceOFzpxe5MxOXyaTKV3ZlMz6ss72Xbx4MQkJCVy+fJktW7bg5OSUbgY2/DcO0pbZEhERESnuVNJBbkvW2+7T3t63fft2Ll68iI+PD2vWrKFhw4a2diEhIUycODFdP5UqVbLVUbQ6dOgQ/fv3Z/v27fz444/cfffdALYPjN999x29e/fOl+PKjCP3nRnrB7vExESSkpLsPuhl9pRx6wdZs9lst9xaeqMoadmyJUFBQZw5c4affvqJjRs34uLikq72oIgUPflxC/+Nvv32W2bNmsWvv/7K8ePHuXLlCgkJCezatYvnnnuO5ORk263zeXXRb8aMGcycOfOWY89MXsVpTaqHhYXlOhZPT0/q16/P+PHjiYmJoUGDBulmuu7atQtILd8THBxst65Hjx5MmjSJgwcPEhcXh7u7e65jEXuNGzematWqHDt2jFWrVnHx4kVSUlJo06aNXcklK+s4CAwMLOhQpQjIh7LXIiKOpRq+cp1m+Mpt6aeffgKwuxXx/PnzAFStWpW7777brhZrZh9CV6xYkW5ZzZo1bTVp094+aK0VvHDhwnTb5HdduYLYd04THNa6yCkpKenqCi9fvjzDbax1kW98enpObkUuLAwGg+221IEDB5KSkkK7du0ynDElIkWLNbmXtryBlTX5lDYBmHZWZnYvaHl4ePDyyy/z119/ce7cOcxmM5cuXbLV17XWw4X/LvoNHTo005nK06dPz/K4fvvtNwCefPJJIiMjs5zpbL2Qd+Ns5swu6qW9OJlZnP/3f/+XZZwlS5YEUsse3Er5ocjISGbNmgWk1va9kfVnldX5L20M1p/1je9B2tfWB5HdTE7f28wumELRvGhqvUvmxx9/vGk5B4CzZ88C/40LERERkduBEr5SrKWtAbhp0yZ+//13nnrqKb744gvA/sOBdXbOzp07efnll1mzZg3r16/nyy+/tD1QJK133nmHzp0706ZNGxYuXMjx48c5cuQI8+bNY+7cuUBq8tfK+uFk4cKF9O/fnyVLlrBq1SpGjBhBhQoVWLRoUX69DQWyb+uMqmXLlrF58+YsP0BWq1bN9kC0AQMG8NVXX7Fu3TqefPJJdu/eneE21tuGP/30U77++msuXbrE5s2bbQ+TKWqst6VaP7CrnINI8VC1alUA/v33X7v6oxaLhTVr1ti1gdSZh9YEXnYvaK1bty5dcq9UqVJ069YNyPiC49KlS9MlCHMyC9Z6YfSBBx7Ax8fH7rgyYr2wt2HDBrvly5Yty7B9Xl2crFatGj4+PiQkJNzSBcEPPviAyMhIqlevTq9evdKtr1KlCgC7d+/m9OnTdut+/vlnILXsgfUhr4DtvJe25BP893M2Go3Zmoma0/fWesF0//79dgnolJQUduzYkeX+Chvr+XPBggUsXboUg8GQ7iGsVn///TdAuofwiYiIiBRnmustxZr1dtaM1K5dm1dffdX2ulmzZjRq1Ijt27fzv//9L8Mkb1rWmnDr16+3PS07rYoVK9o9ybxnz548/PDD/Pjjj3z77bd8++23du1/+uknevToka3jyqmC2HfLli3Zvn07X331FV999RURERE3fWCOwWAgJCSEp556ilOnTjFw4EDb8iZNmrB169Z024wYMYJPPvmE0NBQHnvsMdvytA9FKkoaNWpEhQoVbLOPMqo9KCJFT4cOHfDy8uLatWt07tyZV199FS8vL+bOnWtL6Kb9fTeZTDRr1oy///6bF198EX9/f2rVqsXff/+dYQ3Yv/76i44dOxIUFMQLL7xAu3btcHd358CBA4wfPx6wv+DYo0cPRo8ezenTp2nTpg2jRo2iXLlybNmyhbfffpshQ4akK0+UkeDgYI4cOcK4ceOIiYkhKCiIa9eu2Wb+3qh169Zs3LiRefPmUapUKTp06MCJEycYPXp0hu379OnDwoULbRcne/fujbu7O4sXL+bzzz9nwYIF2TpXOTs707JlS5YuXcq6detsd93kxLVr12zv/UsvvWR3149V165d8fHxISoqig4dOjB+/HjKlSvHqlWrmDZtGgD9+/e326ZLly6sX7+ezz77jDJlytCiRQtOnjxp+7m1a9cuWzN8c/reWi+YXr58mSeffJLXX38dFxcX3nvvvQxnohd2derUoW7durYH/TVr1ozy5cuna3fu3DmOHTtm9xA4ERERkduBZvjKbcXZ2ZlKlSrx/PPPs2HDBrsZSk5OTqxYsYJnnnmGoKAgXFxcKFu2LCNHjuTll19O19dbb73FP//8w8CBA6lcuTJubm4YjUaqVavGiBEj2LJlC/7+/nbbfPfdd8ycOZOGDRvi4eGBl5cXrVu35rvvvuObb77J12PP732PHz+e7t275+ihKE8++SSff/45tWrVsr13X3/9NV26dMmwfenSpfnzzz/p2rUrXl5e+Pr60qtXr0xnNBUF1plcrVu3ts3YEpGizdfX15ZA3b17N4888ghdu3blxx9/BKBfv360b9/ebpsJEybg5OTEsWPHaNu2LaVLl6Znz56Z1iQ1Go0cP36c4cOHU7t2bSpXrkzXrl3Zv38/JpPJrgRBuXLlbPFs27aNfv360bZtW8aMGUNkZCQrVqywmxGcmREjRgCwb98++vXrR6tWrejSpYvtrpYbjRw5koCAAJKTk3nnnXdo3749TzzxBM2aNcuwvfXiJKTWKP6///s/OnbsyOzZszGbzbZyTNnRoUMHAL766iu7B8ll14cffkhYWBjlypWzu8CYlpeXF3PmzMHZ2ZkjR44wcOBA20PbkpKSqFWrFpMmTbLbZvjw4dSoUYOEhARee+012rdvz5AhQ7hw4QIeHh5Mnjw5W/Hl9L2tWrWq7Ti++uorqlWrRnBwMB9//HGRLSVkvXsJMr9DxnpHl/XhuiIiIsWei0vx/ZIcMViy84QMERHJF/fccw8bN27k/fffT/cEeBFxLGtt1k2bNmWaSLtZm99++43//e9/7Nixg8TERKpXr87gwYN57rnncHZ2TtfXb7/9xoQJE9i3bx/+/v4MHDiQunXr8uijj1KpUiVOnjxpa3v+/HlmzJjB8uXLOXHiBPHx8ZQqVYp77rmHMWPGZHj7+vLly5k2bRpbt24lPj6e6tWrM2DAAEaOHJnth4otWrSIqVOnsnv3blJSUrjzzjuZPHky7dq1A+DEiRN2DzDbt28fL730EuvXr8doNPLQQw8xatQo6tatC8DFixftLnalpKTw4YcfMnfuXA4ePIiTkxMNGzZk2LBhOXroaHh4OJUqVeLatWssWrQo3R0U1oexdurUKV3d+Li4OCpXrsylS5f43//+x4svvnjTfW3cuJF3332XTZs2ERUVRcWKFXnooYd49dVX8fb2Ttc+NDSU119/nd9++42LFy/i6+tLy5YtmThxIvXr18/2MWb13l64cMFWygGwJZnnz5/PlStXqFWrFm+//Tbvv/8+K1asYMKECYSEhABw8uRJKleunGE/8+bNY/DgwdSsWZODBw/axTRo0CC+/PJLnn76aT7++GPb8sx+T9atW0e7du0wmUzpSpTc7GcEcOTIEWrUqAHA8ePHbfFamc1mKlWqxKVLl5gzZw6PP/549t5Yua3s2ePoCEREUtWrl0cdZVLiqFi4XjJLskcJXxERB0lISKBixYpcvnyZ06dPU6FCBUeHJCKS77Zt20aTJk0AiImJwcPDI1/2M3r0aKZPn07t2rXZvn07bm5u+bIfcYwNGzbQsmVLGjRokGEd4jfeeIMJEyZQrlw5Tpw4ka1SGXL7UcJXRAoLJXyzQQnfHFFJBxERB1i3bh1PPPEEly5domnTpkr2ikixtG7dOrsH1wEsWbIEgEqVKuVbshfg5ZdfJjAwkP3792dYmkmKNuvD/TIq57BlyxZbOY1JkyYp2SsiIiK3HRXBEBFxAOvtzwDPPfecAyMREckf69ev5/7776ds2bKEhIRQunRpVq9ebXugWdoarPmhTJkyzJs3jwceeCBduQApuo4cOcK6detsJSMySvhGR0fbylyolIOIiNxWVOtWrlNJBxERB3BxcSEoKIjhw4dnWR9SRKQo2rVrFy1btiQmJibdutq1a7N58+YMa9zmteXLl9O5c+d8348UDGudYCDT+r4AmzdvplatWvj6+hZkeFLEqKSDiBQWeVbS4ZFH8qijQuiHHxwdQZGi1L+IiAMkJSU5OgQRkXx111138c8//zBhwgT++OMPoqKiCAoK4qGHHmLcuHEFkuwFlOwtZgwGA35+fnTo0IEPP/ww03aZPWhRRERE5HagGb4iIiIiIiJy29EMXxEpLDTDNxs0wzdH9NA2ERERERERERERkWJCJR1ERERERERERESKOj20Ta7TDF8RERERERERERGRYkIJXxEREREREREREZFiQglfERERERERERERkWJCxT1ERERERERERESKOtXwles0w1dERERERERERESkmFDCV0RERERERERERKSYUMJXREREREREREREpJhQcY98lJKSwvnz5/H29sZgMDg6HBERyYTFYiE6Oppy5crh5KRroRnROU1EpOjQeU1E5DalGr5ynUZCPjp//jxBQUGODkNERLLpzJkzVKhQwdFhFEo6p4mIFD06r4mIiNyelPDNR97e3gD89ddfeHl5OTia/HH58mUiIyMdHQYGg4Hy5ctz7tw5LBaLQ2Px9fWlVKlSDo1B8kdBj3dHjevbcQxfu3aNVq1a2f7flvRuh3Ma6LyWkdvx/4TbTUGN+4Ie17fz2NV5TURE5PamhG8+st7y6uXlVSz/2Lp48SJPPvkkZrPZ0aEA4OTkREpKiqPDwGQy8e2331KmTBlHhyJ5yFHj3RHj+nYewypVkLnifk4Dndcyczv/n3A7KOhxX5DjWmNX5zUREZHblRK+kmuRkZGYzWZmzpxJtWrVHB0OLi4uJCUlOTSGo0eP8uyzzxIZGXlbf7gojhw13gt6XGsMy+1M57X09H9C8VfQ476gxrXGroiIiNzOlPCVW1atWjXuvPNOR4dRKD4YS/FX0ONd41qk4Om8Jrejghr3GtciIiL5SA9tk+v0yFYRERERERERERGRYkIJXxEREREREREREZFiQglfERERERERERERkWJCCV+57Zw4cYK+fftSoUIFSpYsSevWrVm2bFm6drGxsYwfP546derg6+vLXXfdxbRp01R3ToqU7I53ESm6svt7vn79etq1a4ePjw/+/v7cf//97NmzxwERi+Te3r17ady4Ma6urvTs2dPR4YiIiBQuLi7F90tyRAlfua2EhobSqlUrFi5cSGxsLO7u7mzatInu3bvzww8/2NqlpKTQpUsX3n33XY4fP06JEiU4cOAAY8aMYfDgwQ48ApHsy+54F5GiK7u/54cOHaJLly78/fffuLu74+LiwsqVK2nfvj1nzpxx4BGIZN+xY8do1qwZu3btcnQoIiIiIoWaEr5yW5kyZQqXLl2iV69eXLhwgdOnTzNz5kwAZsyYYWu3du1aNm7cSNmyZTlw4AAnTpxg06ZNeHh48N1333H06FEHHYFI9mV3vItI0ZXd3/MpU6aQkJDA8OHDuXjxIhcvXmTAgAFcvXqV2bNnOyh6kZyJjo6mdOnSvPTSS44ORURERKRQU8JX8t13332Hq6src+bMYd26dXTr1o2SJUtSunRp+vfvT2hoaIHFEh4eTosWLQgJCcFkMgEwYMAAAE6ePGlrd+jQIQAeeeQRgoODAWjYsCEdO3YE4PDhwwUWsxQtRXG8i0jOFMXf83379gHw3HPPYTAYcHZ2ZuTIkQBs27atwOKVoqmwjPkqVaqwZ88eOnXqVCD7E8mtc+dOUr++gWbNvLLVfvHiedSvb2DEiAfypX1O5Gff+SEn73ViYiIffzyRRo2M1K9vYNWqhbZ127ato359Aw89VDfHMYwfP5D69Q288cbTOd72Rlu2rOH553vQoUM5GjUy0rKlP0OGtOX77z8kLi72lvvPT6tWLaR+fQNDhrR1dCgighK+UgB2794NwOrVq+nRowe+vr4MGTKEoKAgfvjhB5577rkCi2XOnDn8+eef1KxZ07bs4MGDAJQtW9a2zM/PD0it45tWTEyM3XqRGxXF8S4iOVMUf88TEhLSbWuxWDJdJ5JWYRnzPj4+eHp6Fsi+RAqDLl2CqV/fwL59/zg6lGLhmWc68dFHISQlJeZZn6dOHWHp0vkYjSaeemr8LfU1Z847PPVUB9au/YXQ0AskJSUSHX2Vf/75k7ffHs5DD9Xm0qVztvZFLTkvBcTRdXZVw7fQ0Dsm+c76IWHv3r3s3LnTNmM2Pj6emjVrsnr16ptu/8Ybb2RrP05OTqSkpDBy5MgcJWTfffdd4L8ZUQBdu3YlMDCQb7/9loceeojmzZuzZMkS1qxZwx133EHTpk2z3b/cXvJ6vFvHdWbyYryLSM4UxfNarVq1+Pfff5kxYwYzZswgJSXFVvrBGr9IZvJyzGd1XoOcn9tEiroHHniUzp374OTk7OhQirVz545z991tuHLlIidPHsqTPj/9dBLJyck88sgwypQJynU/p04dYfbs1wC4//4BDBjwPBUqVCE29hqbN6/myy/fIzi4JqVLl8+TuEWk+FPCV/Ldnj17MBgMfPvtt3YfKt3c3KhYsWKWTwifNGlSjvb32GOPZftDwvz58/nll1+oXLkyw4YNsy338/Nj4cKFdOjQgS5dutiWlyhRgkWLFuHsrD/GJGNFcbyLSM4Uxd/z5557jh9//JEPP/yQ7777jpSUFK5evQpA8+bNcxSP3H4K85gXKQ6cnZ31+aIAvPrqR7Rs2YUhQ9rmScL35MnDLFv2LW5u7gwZMvaW+tq0aSXJycnUrduEt976CoPBAICPjz8PPjiY++8fQEJC/C3HLCK3D5V0kHx18eJFLl26RPPmzbnzzjvTrT9+/DiVK1e+aR+JiYnZ+rJYLCQmJmZ7ptKBAwcYPnw4zs7OzJ0711b70LrP1157jaSkJIxGI0FBQTg7OxMeHs5bb72Vo/dAbh/5Md6t4zqzr7wY7yKSfUX1vNakSRO++eYbqlevTlxcHKVKlQLAaDTSu3fv7L8BctvJ6zGf1XktJ2NepLjI6Nb8li39OH/+FAD9+jXmkUfuynDbf/75k379mtCkiTsPPFCN77//MMN2J08e5qWXetO2bUkaNTJy//1VmT59TI7qwiYmJvDxxxPp1q0Gd99tonXrAJ5//kEOHdqdrm1SUhIzZrxC+/ZladrUk6FDO3Hq1BGaNfOifn0D586dBGD48K7Ur2/g558/T9dHr171qF/fwIYNK7Ido9Xu3Zu4+24Td99tYvv29QC0bNkli63Ss9albdnSnxMnDtqt++STN0hOTqZ37+GULHlr5dJiYqIAqFKlli3Zm5arqyuent621998M4PXXx8MwF9//U79+gb++OMX23prOZCdOzcwZcrztGkTSIsWPrz0Um/Cwi4D8P33H9KlSzBNmrgzcGBL/v13a7bjPXnyMMOGdaFpU0/atSvN+++PJSkpKV27m9VYHjKkbbpayh99FEL9+gbefnsEq1f/TI8edWjc2I1HHrnLNg4OHdrN44+3oWlTD7p0CWbevP+l6/u11wZRv76BL798jx9//ISuXatcP857bCVStm5dS9++d9O4sRv/93938OuvX9n1kV9jU6SgKOEr+WrXrl0AtG/fPt268+fPc/nyZerXr1/AUaXW5u3bty8xMTGMHz+ee+65x279559/zl9//UWrVq04ffo0x48f59ChQ9SsWZNvvvmGFSv0H7ukV1THu4hkX1H+PX/kkUfYv38/UVFRdO/eHYDevXsTEBBQ0OFKEVJYx7yIwMGDOxk6tCP79m3DbI7nzJljvP32cJYv/96u3ZkzxxgwoCkrV/5ARMQVkpISOXv2OPPmTWHYsM4kJydna38vvvgwH30UwunTR0hMTCAyMpy1axfz2GMt0iV9P/54InPnvsuVKxeJj49l06aVPP54a+LiYuzaPfDAYwD8/vt8u+WnTh3hyJF/8fcPpGnT9P//3Mzly+d54YWeJCYm8Prrn9KoUescbW915Mi/vPbaIFxcXHjvvR+pXPkO27oTJw6yfPkCPDy8GDjwxSxL1WSlfPkqAPz119J0ieVb8frrg5k//32uXg0jJiaalSt/4JVX+jJ//ge8/fZwzp8/hdkcz65dG3j66Q5ERFzJss/ExESGDevMhg3LiY+PJTz8Ml98MZm33nomz+LeuHE5L77Yi+PH95OQYObQod2MHNmNLVv+YMiQtmzfvp74+DjOnz/F9Okvs3Dhpxn2s3Dhp7z55lDOnTtx/Tg3MnRoRzZvXs2wYZ3Zv387CQlmTp48xPjxA+0SuPkxNguEo+vsqoZvoaF3TPKVteZbgwYN0q3buXMnAHfddddN+8iPWocjRoxg3759tGrVirFj099+Y61FN27cONsH4UqVKvH888/zzDPPsHLlSj0hWtLJj/GeFzV8sxrvIpJ9RfW8llZERASffPIJzs7O+j9BspTXY141fEWy548/LtG5cyXCwi4xd+5f3Hlnk3RtQkPPM2rUFLp1G0hkZBjvvDOCrVv/4PPP36Jz5//u3vj444lER1+lUqUaTJo0j4oVq/P338t4440n2bHjL9as+ZmOHR++aTxbtvzBunVLcHJyYuzYWbRv35PLl88SEvIEBw/uZObMccya9TuQOhP422/fB6Bbt8cYMeJN4uPjmDlzHKtX/2TX7733Poi3ty/bt//JpUtnKV26AgCrVv0IQIcOvXDJQaInIcHM6NEPERp6gSFDxtK9+8Bsb5tWZGQ4zz//IHFxMYwf/zHNmnWwW//FF++SkpJCbOw1OneuREpKMo0b38urr35IUFDVHO+vdesHKFcumPPnT9KzZ10aNGhJgwataNCgJXfd1cJudi9A377PYjS68dZbz9C0aXtmzvwNV1djun7Pnj3Gd99tp0yZiqxY8b1tjGzd+gf/+98PNGlyL/v2/cOrrz5KREQo338/m6FDJ9w01tWrF3Lu3AmMRhNvvDGPZs06cPjwbp56qsNNt8uJM2eO8eabX9Kq1f2cPn2EsWP7c/bscZ56qj3duw/i2WffIj4+lilTnuOvv5YyZ8479Or1VLp+Tp8+wgcf/Mpdd7Vg375/GDu2H1evhvH00/fxxBPj6NdvJGFhl5g48Un27t3KF19M5p57Uj/n5/XYFCloGp2Sr6wfEjL6IJDdDwl5Xfdt7ty5fP311/j7+/Pll19mWC/L+sRyJyf7SfDW22vMZnOOYpLbQ1Ed7yKSfcXh93zWrFlER0fTv39/qlevnqNY5PZTGMe8yO3AaDRhMrkBYDK5ZZjMK1cumEGDXgIgIKAUISFz6Nq1MseP7ycxMRFXV1cAtmxJnczy8sszqF8/tW57t26PsmfPJn744SO2bFmTjYRvah8dOvTikUeese1z/PiPGTCgKdu2rcVisWAwpJZriImJxt3dk9de+8R2HOPGzU6X8DWZ3OjQoReLFs1h2bIFtuOx3ubfuXOfHL1vb7zxFP/+u4UOHXry7LO5K8WXnJzMs88+wNmzxxkwYBQPP/y03frY2Bhb0g9Sk8yQWod3xIj7+fnnfTn+m9vDw5M5c9YxY8YY1q79hX/++ZN//vkTAFdXI/fd9zDDhk20JZOdnZ1t76vR6Gb7/kYTJnxOrVoNAejTZzirV//Etm1radOmm+1nfs89nRg8+GWmTXspw/IcNzpy5F8g9eFyXbqk/nyaNm3PE0+M4/PP387RcWemUaPWdOuWOsPWzy+AoUMnMH58avJ+3LjZuLt7APDGG/No164U58+f5Nq1KLy8fOz66dbtMdq0SS2V0qJFR/r2HclHH03A29uXESPexGAwEBBQmnHjZtOvX2MOH/7v+PN6bIoUNJV0kHy1e/duAgMDCQpK/8RS64eErG4DzMtah3v37uW5554D4NNPP80wLvjvg8s777xDREQEAKdOnWL69OkANGzY8KYxy+0pP8b7rdTwze54F5HsK6rnNatr164xa9as6zO0NLtXspbXY141fEXyTtWqdexely8fjMnkRnJyMmFhF23Lw8NTa7bWqGH/u1qz5l0Adm0zY+2jZk37Pu64I7WP+Pg4oqMj7forW7aiXSIyIKA07u6e6fq2JvaWLk29df7s2eMcPLiTkiXL0bBhqyxjs1qwYJatDmu7dg9mWAs3O2bMGMPu3ZuAjJN669f/RlxcDG5u7syevZSNG6OYPHkBLi6unDx5iLVrf7G1TUpKwmyOz/DrxlIa5cpVYsqU71i79jKffrqa559/l3btHsTZ2YWlS+czcOA92Sq5kFb16va114ODawL//eytKleuBcDly+ey7PPKldSfb6VKNeyW33FH+jtBcqtaNfu4K1VKjbtMmSBbshegRImS+PsHAhnHntnxV69ez258WI8/KiqC+Pg42/K8GpsijqCEr+Sb2NhYjh49mumsj507d1KpUiVKlChRYDGNHTuWuLg4XFxcGD9+PHXr1rX7sho+fDjly5dn3bp1VKhQgapVq1KzZk0OHTpEvXr16Nu3b4HFLEVDUR7vIpI9xeH3/NNPPyU8PJxHHnmEmjVrFlicUjQVxjEvIjfn5JQ6szRtMtH6/Y2zhK2vM3rY1o2Sk5Nu2kfaNtayLQZD9tINDRu2oly5YA4d2s3Ro/tsMyg7deqd7o7LzMTFxfDee6Nsr2fPHm+XuMuuY8f28dVXU22v3313JBaLxa7NgQPbAejYsTctW3bB09ObLl360LHjIwB2s2Q/++xNmjRxz/Drt9++zjAGLy8fmjZtz+DBLzNjxiKWLj1OcHBNwsIusWTJlzk+prSs4+PG99U6IzklJet6zhZLSoZ95CdrfBntM6Mxn9N+0s7ITvse5MXYFHEUjVDJN3v27CElJSXDmm9hYWGcOXOmwB/yERub+hTapKQkDh06lO7LqkyZMmzcuJFBgwYREBDAxYsXqVChAiNHjuSPP/7A3d29QOOWwq8oj3cRyZ6i/ntuNpuZMWMGTk5OjBs3rkDjlKKpMI55Eck5azIrMTHBbrn1dXbqkDo7u9y0j7RtrIkwa2IwrYyWGQwG7r9/AJA6kzK3t8y7upr4/vudtGzZhfPnT/HFF5NztL1VYGAZfv/9ONWr38m//25h8eJ5dusjI8MBqFixmt1y60PdsjNjOicCAkrb3ovTp4/kad+5YU3k31iPPaP67Naxd2PSvKjIq7FZoBz9YDU9tK3Q0Dsm+aZZs2YkJiZmuC4gICDTdflpzZo12W5brlw5Pvvss3yMRoqToj7eRSRrRf333GQycfr06XyMRoqbwjjmAdq0aeOwfYsURQEBZbh8+RxHjuyhVKlytuXWeqUBAWWy7CMwsMz1bfbYLbfOZnVzc8fb2/d627IAXLx4hoQEM0ajyfY6s1m33bo9xmefvcmcOe8AUKFClQwfVHcz77wznzvuuIuXXprO5s2rmTdvCt27D6JChco56mfGjMVUqFCZl19+nyefvJf333+F9u0fsh2fr2/qXQ0XLtifUy9ePHN9fYBt2TPPhPDMMyFZ7jMmJprTp49Sq1bGZRGsdXM9PLxydCz5oWTJ1J/vjcnn/fu3p2sbEFAGZ2dn4uNjuXo1DD+//96b7JSPKAzyYmyKOIJm+IqIiIiIiIjksYSEjGu3ZlbD9Ubu7qnJvRMnDqabWZsTzZt3BOB//xvFnj2biYi4wu+/z+eXX74AoFmzDra21oTi5ctniYuLtc3MtPaxevVCFi78lPDwUA4c2Mlbb6U+wK1x43ttNVHLlQvGy8uH2NhrvPnmM1y6dJZLl87y5pvPZBpjpUrVqVevme11p069c3yc7dr9H5Bap7Vfv5GYzfF2ZR6yy5rMa9KkHR069CQ8/DIffTTBtr5Bg9Tarb///g1r1y7m6tUw1q5dzO+/fwPA3Xe3zdH+UlJSeOWVfgwY0JQ33niabdvWER4eSlRUBAcO7GTSpKGsWfMzAC1bdrVtZ/1ZXbp0hpiY6Axn2OYHay3o33//hhUrfiAqKoLt29czb96UdG1dXV2pUqU2AO+//4rtYt3Spd9y+vTRAon3VuXF2BRxBCV8RURERERERPJYcnJypvVbb1bD1cr6EKxXX32U/v1zP6Pw6adfx9e3BCdOHOTRR5vTtm1Jxo0bgNkcT8OGrbj33h62trVqNcTZ2ZlDh3bTrJkna9cuBqBRo9a0b/8QycnJTJr0NO3alaJPn4bs378dNzcPnn32LVsfrq6uPPbYiwAsXjyXjh2D6NgxiKNH/71p3VPrA7Ig57fM3/gwuKeffp2AgNKsXbuYDRuWZ7ufGx+EN3r0e5hMbnz//WyOHNkLQOvW99Ogfk9lwwABAABJREFUQUvi42N5/vkHadMmkOeff5D4+FiaNLmXFi065ij2pKREypULJjk5iZ9++pQnnmhHu3alaNWqBH36NGThwk8A6N17GE2b3mvbrkaN+hgMBg4f3kOLFj6sW7ckR/vNrfbtHyI4uCZmczwvv9ybVq1K8PjjbShbtmKG7YcMSS0j9fPPn3PPPT60bOnPuHED0tWDLsxuZWyKOIoSviIiIiIiIiKFzPDhk2jYsJWtJEJulS8fzNdfb6Zjx0fw8wvAxcWF8uUrM2jQy3z44XK7B1ZVqFCFsWNnU7JkuXT9vPvudzzzzEQqVqyOi4srPj7+tG3bna++2kjNmvb1vJ94YhxPP/06pUtXwGRyo2HDVsyevQwXF1cg47rBDRq0BKBKlVrUqFHvlo7Zy8uHZ599+3rcz+V6hnT58sEMHPgSSUlJTJ78LJBao3j27KX06zeSUqXK4+LiQunSFRgwYBTvv7/ENtM5u4xGE2PHzmTBgu306zeSKlVq4+3th4uLC4GBZWjTphvvv7+YceNm221XqVJ1Ro2aYiu3UVBcXFyYPXsZbdt2x8PDCx8ff3r3HsZzz2VcM7lLlz68+uqHBAVVJSUlhYCA0rz77nfUr9+8QOO+FXk5NvOdo+vsqoZvoWGwFNXq2UVAVFQUvr6+7Ny5E29vb0eHk+cOHTrE448/zrJly7jzzjsdHQ4uLi7ZesJsfvr333/p0qULX3zxhZ5+Xsw4arwX9Li+XcdwdHQ0DRo0IDIyEh8fH0eHUygV93Ma6LyWkdv1/4TbSUGP+4Ia17f72NV5LXv27Mm6jeSdo0f30bNnXVxcXNi06Vq6RPb8+R8wZcpzDBv2Bk8//ZqDohRJryDGZr28yiMX5wfzvv12rjZbunQp06ZNY8eOHZjNZmrWrMngwYMZPnz4Te86uNHOnTtZtGgRf/75J/v27SMyMhJ/f38aNWrEU089RY8ePTLcbt68eQwePPimfS9btozOnTvn6LiyohS5iIiIiIiIiOSZNWsWMXv2a7zwwnvUq9eckycP8fbbw4DUGrdpk72JiQkcPryHr7+eCuiWeSk8NDaLvsmTJzN27FgAqlSpgpeXF7t372bkyJGsXr2aRYsWZSvpe+zYMRo2bGh7XblyZYKDgzl+/DjLly9n+fLlDBw4kC+++CLT/kqVKkX16tUzXOfv75+Lo7s5JXxFREREREREJM8sX/4dx47tY9iwLnbL3dw8eP75d+2Wff7523z88UQg9QFylSplnBARKWgam0Xbpk2bGDduHE5OTnzzzTf07dsXgN27d9OpUyeWLFnCtGnTePHFF7Psy2KxULZsWZ5//nkeffRRypYtC6Q+dPHDDz9k5MiRfPnll9x9992MGDEiwz66dOnCvHnz8uz4sqIaviIiIiIiIiKSZ955Zz4jR75NpUo1cHU14u8fSIcOPfnmmy3UqtXQrq3BYMDT05tWrboyadKXDopYJL0iOTYdXWe3ENXwffPNN7FYLDzxxBO2ZC9A/fr1mTZtGpA6AzgxMTHLvipUqMDRo0d5+eWXbcleSK3pPWLECJ5++mkAPvvssxzHmV80w1du2dGjRx0dAlA4ah0WlvdC8k9B/4wLelxrDIsUnt8DndekIBXUz7qgxrXGrohjubi4MGTIWIYMGZtl26FDJzB06IQCiEokZzQ2i66oqChWr14NwJAhQ9Ktf/jhh3nmmWcICwtj7dq1dOzY8ab9ubm53XR9x44d+fjjjzl8+HDug85jSvhKrvn6+mIymXj22WcdHQqQemUlJSXF0WFgMpnw9fV1dBiSxxw13h0xrjWG5Xal81rG9H9C8VbQ474gx7XGroiIyO1p586dJCQk4ObmZld718rV1ZXGjRuzZs0atmzZkmXCNyvx8fEAuLu7Z9pm9+7d9OvXj4sXL+Lj40ODBg0YMGAAVatWvaV9Z0YJX8m1MmXK8O233xIZGenoUDAYDFSoUIGzZ89isVgcGouvry9lypRxaAyS9xwx3h01rjWG5Xal81rG9H9C8VaQ476gx7XGroiISPFhNpsxm812y0wmEyaTKV3bI0eOAFCxYkVcMikHUaVKFdasWWNreyt++OEHAO65555M2+zatYtdu3bZXi9evJhJkyYxceJEXn311VuO4UZK+MotKVOmTKH4Q9pgMFClShU8PT0d/sFYiq+CHu8a1yIFT+c1uR0V1LjXuBaR4ig+Po5PPnmDVat+5MKF07i7e1CvXnOGDg2hXr2meb59eHgoM2eOY/3634iMDKds2Yp06zaQIUPG4uzsnB+HKEVJLmrdFhXvvPMOEydOtFs2YcIEQkJC0rWNiIgAwN/fP9P+rOusbXNr5cqV/PLLLwC89NJL6db7+fnx7LPP0qdPH6pVq4avry8HDhxg2rRpfP3114wfPx5fX99MH/aWW8V3JIiIiIiIiIiI5KNRo3qwceMK2+vo6Eg2bFjO1q1/8NVXG6ldu1GebR8fH8eQIW05fny/bdnp00eZPfs1zp49zhtvfJGHRyZSuIwdO5bRo0fbLctodi/8V2LBaDRm2p9127i4uFzHdPr0afr37w/AsGHDaN26dbo2Dz74IA8++KDdsrvuuouvvvqKgIAAZsyYwfjx4xk4cCDe3t65juVGTnnWk4iIiIiIiIjIbWLnzg1s3LgCFxcX3nlnPn/+eYWfftpL/frNSUxM4LPP3srT7X/99SuOH9+Pv38gn366mnXrQhk//mMAFi+ey/+zd9/RUVVfG8efSZk0SAVCJ4HQa0CKNEGRJoKodEQQ9YeIoCAg5aVKEaQJFkQEEQQRRBCQKqB0pEovSeg1ISRAeub9I2ZkTICUSeX7WWvWypx77rn73kxykj1n9g0KOpVh5wpkNQcHB7m6ulo8HpbwTbzJWnR09EPHSywP8ai6u48SEhKiFi1a6NatW2rUqJGmTp2a6jFGjx4tBwcH3blzR7///nua4ngYEr7IFf7++2+9/vrr+vvvv7M6FAAA0o15DQCAx7t8OUhVqxr01lvPKTQ0WJMmva8WLXxUvbqdpk5N+tFqa/v77z2SpPr1W6ply85yd/eSn19F9es30WK7tfb/66+tkqROnfqqdu3n5OGRT+3a/U+1aj37z/ZtVjs3ICdLSbmGlJR9eJi7d++qZcuWOn78uGrUqKFVq1Y9NPn8KK6urqpYsaIk6ezZs6ne/1FI+CLHi46O1pIlS3Tjxg0tWbLkke/gAACQ3TGvAQCQMqdOHZIkOTg46tVXq2j9+h9VqVIt1a3bTJUrP75+bnrdvx8uSfL2LmbRXrBgcUnSvXthVt0/NDRYklS0aEmL9iJFfCVJt2/fTHHsQG5WunRpSQklF2JjY5PtExAQYNE3paKiotSmTRvt2bNHFSpU0Lp169JVisHe3l6SHhpnWlHDFznehg0bzHeWvnPnjjZs2KBWrVplcVQAAKQN8xoAACmTmPD988+1at/+HQ0cOE1G46NX2cXExCg+Pi7Zbfb2RtnYpHxd3ONuQGnt7fnzF5IkXbx4zqL94sWElYEuLq6PHA9PgFx807bU8Pf3l729vSIjI3XgwAHVqlXLYntMTIz27dsnSapdO+VvDsXGxqp9+/b6/fffVbJkSW3cuFH58uVLc5xxcXE6dSqhFEvRokXTPE5yctUK388//1wGg0Hvvvtukm2BgYFq3769vLy8ZDQa5efnp9GjRyebQV+2bJlq1qwpJycn5cmTRw0bNtSmTZsy4xSQSjdv3tSGDRvME6HJZNLGjRt18ybvbALI+ZjXnjzMawAApNypU4clSdWrN9CQIbMem+yVpDFj3lKtWk7JPvbv/yOjQ06XihVrSpKWLJmpPXs26/btW1q69EtzKYfy5atnZXhAtuHq6qomTZpIkubOnZtk+08//aSwsDB5eXmpUaNGKRrTZDKpe/fuWrVqlQoXLqxNmzapcOHC6Ypz7ty5Cg0Nla2tbYrjSKlck/CNi4vT1KlTZWNjow8++MBiW0REhJo0aaKffvpJkZGR8vb2VmBgoEaNGqXhw4db9F2/fr3atWunv/76S25ubjIajfrzzz/VokUL6uhlMyaTSUuXLn1o++PeLQWA7Ix57cnDvAYAQOokrvDt3//TVK3Mzanatu0pb++iun37lt5+u4kaNcqvceN6S5IKFiymqlWfzuIIgexj2LBhMhgM+uabb7R48WJz++HDh9W/f39J0qBBg2Q0Gs3bpk+fLh8fH3Xs2DHJeP369dOiRYuUL18+bdq0Sb6+vo+NISwsTJ06ddLevXst2uPi4jRnzhz169dPktSzZ08VKVIkTef5MLnmN+LPP/+sgIAAtWnTRn5+fhbbFi1apICAADVq1Eg3btzQxYsXtW/fPjk4OGjq1Km6f/++ue/YsWMlSbNmzdK1a9cUHBysgQMHKjY2VhMnTszUc8KjXb9+XSdOnFB8fLxFe3x8vE6cOKHr169nUWQAkH7Ma08e5jUAAFIuPPyOrlwJkrd3UVWqVDPF+40YMUd790Yk+6hRo6GkhGRMVFRksg9r19lMDUdHJ3399SbVqdNEDg6OKly4hHlb69bdn4ikN5BS9erV09ixYxUfH6/OnTurVKlSqlq1qqpXr67r16/rhRde0IABAyz2CQ0N1fnz53Xt2jWL9l27dmnmzJmSJCcnJ7311luqX79+so8HxcfHa8mSJapdu7Y8PDxUvXp11apVS/ny5dPbb7+tyMhItWjRQjNmzLD6+eea4h6ffvqpJCX5ZknS0aNHJUk9evSQi4uLJKl69eqqW7eutmzZotOnT6tatWrmvvb29urVq5ckyWAwqE+fPpo8ebKOHTuWCWeClPL29lb58uV16tQpi3+ObWxsVLZsWXl7e2dhdACQPsxrTx7mNQAAUu706YRyDjVqNJTBYEjxfmPGvKVVq75Ldts332xRzZqNtHr19xoxokeyfXr1Gql33hmV6nitxcenrGbP3ihJCgo6pZdfrih7ewd16vRelsWEbIQavhaGDRumqlWratq0adq/f7+uXbumypUrq0ePHurTp49sbW1TNE5UVJT564sXL+rixYsp2s/FxUWTJk3Szp07dfToUZ07d04RERHy8vLSCy+8oG7duqldu3ap+h2WUrnilbBt2zbt3btXderUUb169ZJsDw0NlZRQw+NBic8Tt0sJN0dxc3Oz+KYn1w9Zz2AwqH379ubVa/9tz4gfGADIDMxrTybmNQAAUu7kyUOSpLJlq2VZDI+bmzN6+1dfjVZcXJy6du0tT8/8j+wLPKlatWqV4hsgjxo1SqNGjUrS3qhRozSVV7O3t9fAgQNTvZ815IqE7+TJkyVJH374oSRp9+7dioyMVP369WXHuxu5Wv78+dW0aVOtX79eJpNJBoNBzz//vPLnZ7IDkHMxrz25mNcAAEiZxPq95cr5p2q/sWPna+zY+Y/s06ZNd7Vp0/2xYzk55ZEkXb9uudrv2rULkiQXl7wZtn9AwAmtX/+jHB2d9frrWZNQApB95fj/Go8fP661a9eqZMmSatu2rSSpY8eOOn/+vG7fvi13d/dMiyUqKspimXdYWJikhHflWJWTcZo1a6bdu3crNDRUbm5uatasGdcbuULi65jXc8bLTtc4u8xrzGlZh3kNuRXzWubhGuNJkFjSIStX+FapUkeStH37Wq1d+4Pq1m2mW7euacaMjyRJlSrVtugfExOt+Ph42dnZy9bWNtX7P+jLL0cpPj5e7dr1kpdXgYw4PQA5WI5P+H766acymUz64IMPsrxA+YQJEzR69Ogk7b6+vkk+dgvr6tevn7788ku98847KleuXFaHA1hVSu7+ifRJTGZmB9llXmNOy1rMa8jNmNcyXnaa14CMEBsbq3PnjqlAgSLy8MiXZXH4+9dT3brNtHPneg0Z0sVim729UW+/PdyirVevpvrrr20aM2ae2rTpnur9E505c1QbN/4kR0cnde/O6l48gE8D4h85+pVw5coVLVq0SJ6enurRI/mC6plpyJAh6t+/v/l5WFiYihUrpsDAQOXN++iPciB9vL299d133ykwMFABAQFZHQ5gFQaDQb6+vgoMDExTvSCkXHh4eFaHICl7zWvMaVmLeQ25EfNa5sku8xqQUQIDTyo6OipLV/cmmjZthb76arQ2bvxJ165dlKOjk6pUeVrvvDNKFSrUyJD9Z88eLZPJpFdf/Z/y5Sto7VMCkAvk6ITvjBkzFB0drV69epnvUp6VHBwc5ODgkKTdZDLxR20m4VojN+J1nfGyy/XNTvMac1r2wPVGbsTrOuNxfZHblS5dSYcPZ4/XuaOjk95/f6Lef3/iY/vOnbs1Xfsn+vTTn1ITIoAnUNbWQEinDRs2SJLGjx9vriloMBh0/vx5SZKHh4dCQ0PN9Q7/+9GmxOceHh7mNjc3N92/f19xcXGP7AcAgLUxrwEAAAAA0itHJ3wrVKigGjVqJHkkKlWqlOzs7FSxYkVJ0rx583Tv3j1J0oEDB7Rz507Z29urdOnS5n0qVqyomJgYffXVV5IS3h2fNWuWeRsAABmFeQ0AAAAAkF4GUy78vI+Pj4/F3cwjIiJUoUIFBQUFydnZWV5eXrp8+bLi4+M1ePBgTZz470cnfvvtN7Vs2VJSQv28mJgYhYSEyM7OTgcOHFDlypVTHEdYWJjc3Nx08OBB6h1mMIPBoJIlSyogIICPsCHX4HWdecLDw+Xv7687d+5kyxuSZYd5jTktc/Hzj9yI13Xmye7zWnZx5EhWRwAACapUsdJAn31mpYGyob59szqCHCVHr/BNKScnJ23cuFEvv/yyHBwcdPXqVfn4+GjUqFH6+OOPLfq2aNFCixcvlr+/v0JDQxUZGan69evrt99+S1WyFwCAjMK8BgAAAAB4mBx907aHCQoKStLm5+en5cuXp2j/jh07qmPHjlaOCgCAtGFeAwAAAACk1BOxwhcAAAAAAAAAngQkfAEAAAAAwBPlyy9HqWpVg8aP75PVoaRYZGSEZswYolat/FSjhlH167urd+8WOnJkT6rHGj78dVWtatCYMf97bN+7d8NUt66rqlWz0fnzZ9ISOjKLnV3ufSBVuGIAAAAAAADZ3AcftNXOnevNz8PD72jHjnXau/d3LViwUxUq1EjROOfPn9HatYtkNDro7beHP7b/8uVf6969cD3zzIsqUaJ0muMHkHlY4QsAAAAAAJCNHTy4Qzt3rpednZ0mTFikbdtuafnyo6pa9WnFxERrzpxxKR7r66/HKi4uTq+88rYKFiz2yL6xsbFavHimJKlr1w/SdQ4AMg8JXwAAAAAAgGzs778TyjbUr99SLVt2lru7l/z8Kqpfv4kW2x8nKOi0fvvtBzk6OqlnzyGP7b9x40+6evWCypSpolq1Gqf9BABkKhK+AAAAAAAg3VaunK+qVQ3q06eVdu7coPbt/VWzpqPatCmn1asXpnq82NhYNW7srapVDfr7770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      "text/plain": [
       "<Figure size 1600x1500 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# r-pearson for variables with RC\n",
    "r_Pvals = []\n",
    "for i in range(len(DF_RC.columns)-1):\n",
    "    r_Pvals.append(stats.pearsonr(np.array(DF_RC.RC), np.array(DF_RC.iloc[:,i+1])))\n",
    "    \n",
    "\n",
    "fig = plt.figure(figsize = (16,15))\n",
    "k = 0\n",
    "\n",
    "for i in [1, 2, 3, 4, 7]:\n",
    "    \n",
    "    if k<2:\n",
    "        plt.subplot2grid((3,3), (0,k))\n",
    "    elif k < 4:\n",
    "        plt.subplot2grid((3,3), (1,k-2))\n",
    "    else:\n",
    "        plt.subplot2grid((3,3), (2,k-4))\n",
    "    k+=1\n",
    "    \n",
    "    plt.ylim((0,462))\n",
    "    ax=plt.gca()\n",
    "    ax.set_facecolor('gainsboro')\n",
    "    ax.set_axisbelow(True)\n",
    "    \n",
    "    ax2 = sns.boxplot(y = DF_RC.RC, x = DF_RC.iloc[:,i], palette = sns.color_palette(\"Set1\"), zorder = 2)\n",
    "    \n",
    "    for j in range(len(DF_RC.iloc[:,i].unique())):\n",
    "        if i == 3:\n",
    "            plt.text(j,440,'$n = $'+str(sum(DF_RC.iloc[:,i] == j)), ha = 'center', va = 'center', bbox = dict(facecolor='w', alpha=0.9), fontdict = dict(family = 'bahnschrift', fontsize = 14))\n",
    "            ax2.set_xticklabels(['>1980', '<1980'], family = 'bahnschrift', fontsize=14)\n",
    "        else:\n",
    "            plt.text(j,440,'$n = $'+str(sum(DF_RC.iloc[:,i] == j)), ha = 'center', va = 'center', bbox = dict(facecolor='w', alpha=0.9), fontdict = dict(family = 'bahnschrift', fontsize = 14))\n",
    "            ax2.set_xticklabels(['No', 'Si'], family = 'bahnschrift', fontsize=14)\n",
    "    ax2.set_yticklabels([0,100,200,300,400], family = 'bahnschrift',fontsize=14)\n",
    "    if (k != 1) and (k!=3) and (k!=5):\n",
    "        ax2.set_ylabel('')\n",
    "    else:\n",
    "        ax2.set_ylabel('$C_{Rn}$ [Bq/m$^3$]', fontsize=16, family = 'bahnschrift')\n",
    "    ax2.set_xlabel(Variables[i-1], fontsize=16, family = 'bahnschrift')\n",
    "    ax2.grid(color = 'w', zorder = -3)\n",
    "    \n",
    "ax7 = plt.subplot2grid((3, 3), (0, 2), rowspan=3)\n",
    "\n",
    "Val = []\n",
    "for i in range(len(Variables)):\n",
    "    Val.append(Variables[i] + '\\n$r$ =' +str(round(r_Pvals[i][0], 2)) + '\\np-value =' +str(round(r_Pvals[i][1], 2)))\n",
    "\n",
    "ax = sns.heatmap(pd.DataFrame(DF_RC.corr()['RC'].iloc[1:]), center = 0, vmin = -1, vmax = 1, cmap = 'seismic')\n",
    "ax.set_yticklabels(Val, rotation = 0, x = 0.52, ha = 'center', family='bahnschrift', fontsize = 15)\n",
    "ax.figure.axes[-1].set_yticklabels(np.arange(-1, 1.1, 0.25), size=16)\n",
    "ax.tick_params(length = 0)\n",
    "ax.set_xticklabels([''], rotation = 0)\n",
    "ax.set_title('Correlation coefficient with\\nrespect to RC', family='bahnschrift', fontsize = 19)\n",
    "\n",
    "fig.savefig('Figures/Caracterization.png', dpi = 300, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d330d8c",
   "metadata": {},
   "source": [
    "### Principal component Analysis\n",
    "\n",
    "In order to understand the relationships between independent variables and between them and the dependendent variable, a Principal Component Analysis was performed and the BiPlot is plotted with the scores and the eigen vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3bb7beab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_PCA = DF_RC\n",
    "\n",
    "# Standarization\n",
    "df_PCA = df_PCA.apply(lambda df : (df - np.mean(df))/np.std(df) )\n",
    "\n",
    "Variables.insert(0,'RC')\n",
    "df_PCA.columns = Variables\n",
    "\n",
    "df_ = pd.DataFrame(df_PCA.corr().iloc[0,1:])\n",
    "\n",
    "n_PCA = 5\n",
    "pca = PCA(n_PCA)\n",
    "A_postPCA = pca.fit_transform(df_PCA)\n",
    "A_postPCA = pd.DataFrame(A_postPCA)\n",
    "\n",
    "# Name the columns\n",
    "cols = []\n",
    "for i in range(len(A_postPCA.T)):\n",
    "    cols.append('PC'+str(i+1))\n",
    "\n",
    "A_postPCA.columns = cols\n",
    "\n",
    "EigenVectors = pd.DataFrame(pca.components_.T)\n",
    "\n",
    "fig = plt.figure(figsize = (15,10))\n",
    "\n",
    "gs = gspec(10,15)\n",
    "\n",
    "ax1=fig.add_subplot(gs[:,:10], label=\"1\")\n",
    "ax2=fig.add_subplot(gs[:,:10], label=\"2\", frame_on=False)\n",
    "\n",
    "ax1.scatter(A_postPCA.PC1, A_postPCA.PC2)\n",
    "\n",
    "ax1.set_xlabel(\"PC1 scores\", color=\"k\", fontfamily = 'bahnschrift', fontsize = 16)\n",
    "ax1.set_ylabel(\"PC2 scores\", color=\"k\", fontfamily  = 'bahnschrift', fontsize = 16)\n",
    "ax1.tick_params(axis='x', colors=\"k\", length = 2)\n",
    "ax1.tick_params(axis='y', colors=\"k\", length = 2)\n",
    "ax2.set_xticks(np.arange(-6,7,2))\n",
    "ax2.set_yticks(np.arange(-6,7,2))\n",
    "ax1.set_yticklabels(np.arange(-6,7,2), family = 'bahnschrift', size = 14)\n",
    "ax1.set_xticklabels(np.arange(-6,7,2), family = 'bahnschrift', size = 14)\n",
    "\n",
    "plt.grid(True, color = 'w')\n",
    "\n",
    "ax1.axhline(0,c='k')\n",
    "ax1.axvline(0,c='k')\n",
    "\n",
    "ax1.set_xlim(-6,6)\n",
    "ax1.set_ylim(-6,6)\n",
    "\n",
    "\n",
    "for i in range(len(EigenVectors)):\n",
    "    ax2.plot([0,EigenVectors.iloc[i,0]],[0, EigenVectors.iloc[i,1]], c = 'k', lw = 0.5, alpha=0.5)\n",
    "    if (i == 1)|(i == 7):\n",
    "        plt.text(EigenVectors.iloc[i,0]+0.05, EigenVectors.iloc[i,1], df_PCA.iloc[:,i].name.split('dummy')[0], bbox = dict(facecolor='slategrey', alpha = 0.5), fontsize = 12, fontfamily = 'bahnschrift', ha = 'center',va = 'center', color='w')\n",
    "    elif (i == 2):\n",
    "        plt.text(EigenVectors.iloc[i,0]-0.05, EigenVectors.iloc[i,1], df_PCA.iloc[:,i].name.split('dummy')[0], bbox = dict(facecolor='slategrey', alpha = 0.5), fontsize = 12, fontfamily = 'bahnschrift', ha = 'center',va = 'center', color='w')\n",
    "    elif (i == 3)|(i == 4)|(i == 9)|(i == 8):\n",
    "        plt.text(EigenVectors.iloc[i,0], EigenVectors.iloc[i,1], df_PCA.iloc[:,i].name.split('dummy')[0], bbox = dict(facecolor='darkred', alpha = 0.5), fontsize = 12, fontfamily = 'bahnschrift', ha = 'center',va = 'center', color='w')\n",
    "    elif (i == 1):\n",
    "        plt.text(EigenVectors.iloc[i,0]-.1, EigenVectors.iloc[i,1], df_PCA.iloc[:,i].name.split('dummy')[0], bbox = dict(facecolor='darkred', alpha = 0.5), fontsize = 12, fontfamily = 'bahnschrift', ha = 'center',va = 'center', color='w')\n",
    "    elif (i == 6)|(i == 5):\n",
    "        plt.text(EigenVectors.iloc[i,0], EigenVectors.iloc[i,1]+0.03, df_PCA.iloc[:,i].name.split('dummy')[0], bbox = dict(facecolor='darkblue', alpha = 0.5), fontsize = 12, fontfamily = 'bahnschrift', ha = 'center',va = 'center', color='w')\n",
    "    else:\n",
    "        plt.text(EigenVectors.iloc[i,0], EigenVectors.iloc[i,1], df_PCA.iloc[:,i].name.split('dummy')[0], bbox = dict(facecolor='k', alpha = 0.8), fontsize = 14, fontfamily = 'bahnschrift', ha = 'center',va = 'center', color='w')\n",
    "        \n",
    "\n",
    "    \n",
    "ax2.xaxis.tick_top()\n",
    "ax2.yaxis.tick_right()\n",
    "ax2.xaxis.set_label_position('top') \n",
    "ax2.yaxis.set_label_position('right') \n",
    "ax2.set_xticks(np.arange(-0.5,.7,.25))\n",
    "ax2.set_yticks(np.arange(-0.5,.7,.25))\n",
    "ax2.set_yticklabels(np.arange(-0.5,.7,.25), family = 'bahnschrift', size = 10)\n",
    "ax2.set_xticklabels(np.arange(-0.5,.7,.25), family = 'bahnschrift', size = 10)\n",
    "ax2.tick_params(axis='x', colors=\"darkblue\",length = 2)\n",
    "ax2.tick_params(axis='y', colors=\"darkblue\",length = 2)\n",
    "\n",
    "ax2.set_xlim(-.75,.75)\n",
    "ax2.set_ylim(-.75,.75)\n",
    "\n",
    "plt.text(-0.735,.736,'(a)', ha = 'left', va='top', fontsize = 20, fontfamily = 'bahnschrift')\n",
    "\n",
    "plt.text(0.735,.736,'Construction variable', ha = 'right', va='top', fontsize = 12, color = 'w', bbox = dict(facecolor='slategray', alpha = 0.5), fontfamily = 'bahnschrift')\n",
    "plt.text(0.735,.686,'Geologic variable', ha = 'right', va='top', fontsize = 12, color = 'w', bbox = dict(facecolor='darkred', alpha = 0.5), fontfamily = 'bahnschrift')\n",
    "plt.text(0.735,.636,'Meteorologic variable', ha = 'right', va='top', fontsize = 12, color = 'w', bbox = dict(facecolor='darkblue', alpha = 0.5), fontfamily = 'bahnschrift')\n",
    "\n",
    "\n",
    "plt.title('Principal component analysis\\nfor the 30 measurements', y = 1.075, fontsize = 18, fontfamily = 'bahnschrift')\n",
    "\n",
    "ax1.set_facecolor('whitesmoke')\n",
    "\n",
    "ax3 = fig.add_subplot(gs[:,11:], label='3')\n",
    "\n",
    "df_ = df_PCA.corr(method = 'pearson').apply(lambda df : df[np.abs(df) >= 0.0])\n",
    "df_ = df_.loc[df_.columns]\n",
    "df_ = pd.DataFrame(df_.iloc[1:,0])\n",
    "sns.heatmap(df_, cmap='seismic', vmin = -1,vmax = 1, alpha = 1, cbar = False, zorder = -2)\n",
    "\n",
    "ax3.tick_params(axis='x', length = 0)\n",
    "ax3.tick_params(axis='y', length = 0)\n",
    "\n",
    "Val = []\n",
    "for i in range(len(df_PCA.columns)-1):\n",
    "    Val.append(list(df_PCA.columns)[i+1] + '\\n$r$ =' +str(round(r_Pvals[i][0], 2)) + '\\np-value =' +str(round(r_Pvals[i][1], 2)))\n",
    "    \n",
    "ax3.set_yticklabels(Val, rotation = 0, x = 0.52 , ha = 'center', family='bahnschrift', fontsize = 13, color = 'k', zorder = 5)\n",
    "\n",
    "ax3.set_xticklabels([''], rotation = 0)\n",
    "\n",
    "plt.text(0.02,0.1,'(b)', ha = 'left', va='top', fontsize = 20, fontfamily = 'bahnschrift')\n",
    "\n",
    "plt.title('RC\\ncorrelation\\n($r_{pearson}$)',  y = 1.075, fontsize = 18, fontfamily = 'bahnschrift')\n",
    "\n",
    "fig.savefig('Figures/PCA_RC.png', dpi  = 170, bbox_inches = 'tight')\n",
    "\n",
    "Variables.remove('RC')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c16842",
   "metadata": {},
   "source": [
    "## Statistical regression models\n",
    "In this section the log-linear regression is fitted and feature selection is performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee92daaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split datataset in predictors (X) and predicted variable (y) \n",
    "X = DF_RC.iloc[:,1:]\n",
    "y = DF_RC.iloc[:,0]\n",
    "\n",
    "# Add a a constant to the predictors\n",
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05cce01e",
   "metadata": {},
   "source": [
    "### Log-Linear regression\n",
    "The following lines fit a linear regression to the dataset using Ordinary Least Squares Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5645f595",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.5468621886828955\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>RC</td>        <th>  R-squared:         </th> <td>   0.453</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.838</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 May 2023</td> <th>  Prob (F-statistic):</th>  <td> 0.123</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:58:41</td>     <th>  Log-Likelihood:    </th> <td> -24.461</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   68.92</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    20</td>      <th>  BIC:               </th> <td>   82.93</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     9</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -4.5576</td> <td>   14.352</td> <td>   -0.318</td> <td> 0.754</td> <td>  -34.495</td> <td>   25.380</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Basement_dummy</th>    <td>    0.0994</td> <td>    0.341</td> <td>    0.291</td> <td> 0.774</td> <td>   -0.613</td> <td>    0.812</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Age_1980</th>          <td>    0.6932</td> <td>    0.307</td> <td>    2.255</td> <td> 0.035</td> <td>    0.052</td> <td>    1.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lito_Fans</th>         <td>   -1.1148</td> <td>    0.598</td> <td>   -1.865</td> <td> 0.077</td> <td>   -2.362</td> <td>    0.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lito_Shales</th>       <td>   -0.7174</td> <td>    1.089</td> <td>   -0.659</td> <td> 0.517</td> <td>   -2.988</td> <td>    1.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Precipitation</th>     <td>    0.0027</td> <td>    0.018</td> <td>    0.150</td> <td> 0.882</td> <td>   -0.034</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Temperature</th>       <td>    0.8159</td> <td>    1.029</td> <td>    0.793</td> <td> 0.437</td> <td>   -1.331</td> <td>    2.962</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Urban_dummy</th>       <td>   -0.9269</td> <td>    0.683</td> <td>   -1.356</td> <td> 0.190</td> <td>   -2.352</td> <td>    0.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fault_proximity</th>   <td>  5.31e-05</td> <td> 8.08e-05</td> <td>    0.657</td> <td> 0.519</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vertical_velocity</th> <td>   -1.2007</td> <td>    0.704</td> <td>   -1.706</td> <td> 0.104</td> <td>   -2.669</td> <td>    0.267</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.879</td> <th>  Durbin-Watson:     </th> <td>   2.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.087</td> <th>  Jarque-Bera (JB):  </th> <td>   3.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.543</td> <th>  Prob(JB):          </th> <td>   0.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.228</td> <th>  Cond. No.          </th> <td>8.89e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 8.89e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     RC   R-squared:                       0.453\n",
       "Model:                            OLS   Adj. R-squared:                  0.206\n",
       "Method:                 Least Squares   F-statistic:                     1.838\n",
       "Date:                Fri, 12 May 2023   Prob (F-statistic):              0.123\n",
       "Time:                        10:58:41   Log-Likelihood:                -24.461\n",
       "No. Observations:                  30   AIC:                             68.92\n",
       "Df Residuals:                      20   BIC:                             82.93\n",
       "Df Model:                           9                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "const                -4.5576     14.352     -0.318      0.754     -34.495      25.380\n",
       "Basement_dummy        0.0994      0.341      0.291      0.774      -0.613       0.812\n",
       "Age_1980              0.6932      0.307      2.255      0.035       0.052       1.334\n",
       "Lito_Fans            -1.1148      0.598     -1.865      0.077      -2.362       0.132\n",
       "Lito_Shales          -0.7174      1.089     -0.659      0.517      -2.988       1.554\n",
       "Precipitation         0.0027      0.018      0.150      0.882      -0.034       0.040\n",
       "Temperature           0.8159      1.029      0.793      0.437      -1.331       2.962\n",
       "Urban_dummy          -0.9269      0.683     -1.356      0.190      -2.352       0.499\n",
       "Fault_proximity     5.31e-05   8.08e-05      0.657      0.519      -0.000       0.000\n",
       "Vertical_velocity    -1.2007      0.704     -1.706      0.104      -2.669       0.267\n",
       "==============================================================================\n",
       "Omnibus:                        4.879   Durbin-Watson:                   2.499\n",
       "Prob(Omnibus):                  0.087   Jarque-Bera (JB):                3.359\n",
       "Skew:                          -0.543   Prob(JB):                        0.186\n",
       "Kurtosis:                       4.228   Cond. No.                     8.89e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 8.89e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg = sm.OLS(np.log(y), X).fit(maxiter=1000)\n",
    "\n",
    "RMSE = np.sqrt(np.sum((lin_reg.predict(X)-np.log(y))**2)/30)\n",
    "\n",
    "print('RMSE:', RMSE)\n",
    "\n",
    "lin_reg.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d59769f-a4a4-4f08-8a9f-49fb6eab903b",
   "metadata": {},
   "source": [
    "#### Coefficient visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "baa45eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tTNZpugNkZGTUdxVEHbhy5Up9V6HOTZrUDI1GobLxnYpSGjRNmtSsbiomqmzo0KE0btz4rtjmR9z5JGgycYsXL6Zdu3bExMTUd1XELRQWFka7du3uugA5IMCRFSs6YWFhhkZTfuSk0ShYWJixYkUn2UpFCHFLSdBkwn799VdCQkJIS0sziWmpsbGxKIpCmzZt6rsqBqKiohgwYACOjo5YWVnRsmVLli1bVul1+/fvp0mTJiiKwtSpUw3OXb16lenTp+Pt7Y2lpSUeHh48++yzpKSk3DDPkJAQFEXh3//+t8HxtLQ0YmJiGDduXLXvz9R16+ZOREQP+vf3NAqc9F1yERE96NbNvZ5qKIS4W8iYJhM2Y8YMCgsLWblypUxLraHk5GR69uxJRkYG9vb2uLq6cvLkSSZPnoxGo2HixInlXvfTTz/Rs2dPSkpKyj3/3HPPsWHDBszNzWnUqBGXLl1izZo1REdHc/DgwXKD3KysLD7++GPs7e2ZMGGCwbmFCxeyb98+IiIi+Pnnnw22arkbBAQ4Eh7enjffvJ9Dh1LIySnCzs6cTp3cZLFEIUSdkZYmE/Xzzz+zd+9e2rZty4gRI+q7OiZryZIlZGRkMGrUKNLS0khISGDr1q0ARluilJWenk6LFi2YNGmS0bkzZ86wYcMG3NzcOHPmDBcuXCA+Pp7mzZtz6NAhfvzxx3LzXLlyJVlZWYwfPx5HR8NuJktLS956661K63Wnc3GxYuBAL0aO9GXgQC8JmIQQdUqCJhO1bt06AIO1d0T1/fnnnwBMnDgRCwsLAIKCgvDz8yMxMbHCMURdu3bl999/p0OHDkbn9JshBwUF4evrC4CbmxtPPvmkwfmyCgsLWbx4Mebm5kyZMqXcMocOHYqfnx+7du0iLS2tWvcphBDi5knQZKJ+/PFHFEWhX79+Bsd9fX1RFIWjR4/St29fbG1t8fHxITw8vFr55+XlodVqsbKyMpq1tWDBAhRFYebMmeqx/fv3079/f7RaLVqtlk6dOhEREVGlskJDQ1EUxaiOU6dOVTfsLSsqKoqgoCC0Wi22trb06NGDo0ePGqQJDg4u99rr6YOi61fF1r+vKGhydXVVg6zayHPdunUkJCQwfPhwfHx8ys3XzMyMfv36UVJSwu7du8tNI4QQ4taRoMkEZWVlcfr0aQIDA3F1dS03TZ8+fThy5Ag2NjbExcUxbdo0tmzZUuUyrK2tGTFiBAUFBUbBz4YNGwAYO3YsAPHx8QQFBbFz504AbG1tOXz4MMOGDWPVqlU1ucUKnTx5kq5du7J9+3YKCwtxcHBg37599OrVy2AG4T333ENAQIBRN9ftauHChQC8+uqrAERGRnLgwAGjdN27dwfg8OHDdVc5IYQQgARNJuny5csAeHl5VZhm1KhRZGRkkJqayrRp0wBYunRptcrRB0X6IAkgOjqaY8eO0b59e5o3b67W4/jx40RGRpKRkUFiYiKrV68G/gkGasvcuXPJzc1l3LhxZGRkcOnSJZYtW0ZWVpbBliLz588nOjraJNaG2bFjB3/++Sfdu3fnwQcfBKBnz54EBQUZpfX09AT++Q4IIYSoOxI0maDU1FSACluZoHSsk35H8dmzZwOlA5Sro0uXLjRt2pQ9e/aoU+Wvb2XS8/HxUVtBoLR7zNnZmbNnz1arzMpERkai0WgIDw9Xu8defPFFvL291ZYuU6NfXkDfynQjbm6lKypXtnSBEEKI2idBkwnSBwsFBQUVpikbULm4uACGY2kiIiIIDAws91XW6NGjKS4uZtOmTQBs3LgRS0tLdVAzwLVr15g+fTo+Pj5oNBo1WEtPT6eoqOim77eslJQUiouLcXBwUMtRFIW4uDgSExMpLCys1fJutd9++429e/cSGBjIwIEDK02fn58PlM6mE8IUfP755/z55598/vnn9V0VIW6arNNkgvStDfoWp5rIzMzk1KlTlaYbM2YMoaGhbNy4kc6dO6tdXvpADEoHci9atAgLCwv8/f3RaDQAVcq/uvT7fQUEBJR7Pi8vr8IB2rcjfSuTvmWwMvrPXP8dEOJ25+/vj7+/PzY2NrJfnzB5EjSZIHd3d8zNzW/Y3Zaeno5WqwX+2bOs7KDo4OBggoODKy3L19eXbt26sX//fhYtWgQYd83t2rULKJ3V1qJFC4NrL1y4UGkZ+npmZWUZHM/LyzNK6+TkRHZ2NseOHcPK6ubX6KmobP17/fnazNPJyUk99sMPPwAwYcIEowUtMzMz0Wq1Bi2E+s9cP7ZJCCFE3ZHuORNkZWVFhw4dSExMrDBwWrJkCTqdDp1Ox3vvvQdAs2Y128x07NixlJSUsHbtWlxdXRkwYIDBeX3LTtm1g9LT00lOTjZIZ21tDUB2drbBcX2r0aZNm8jNzQVKtyHZvn27UV26du1KYWEh06dPV7snL168SFhYWLlBVmVatmwJwIoVK9SuxG3btnH+/Hk8PT1rFDTp89y2bRuxsbFAaQvR+vXrDc4DtGnThgcffNDopde2bVuDvPVLDXTr1q3a9RJCCHFzJGgyUX369AFQxxpdb+PGjTg7O+Pq6qoGTRUtmliZ4cOHY2trC8BTTz1l1P01aNAgoPQXube3N/7+/nh4eBgFMe7u7ri7uxMTE4Ovr68aADzyyCMEBARw4sQJmjRpQu/evfH39yc9Pd2oLqGhoVhbW/PRRx+h1Wrx8vLC29ub0NBQ5s6dq6abNWsWgYGBla4V9dJLL+Hg4MD69etxdnbG09NTvZ833nhDTacfAzZr1qxKn9e9997LiBEjSElJ4d5778XHxwdPT0+io6Pp1KkTvXv3VtPu3buX3377zegFpS2De/fuVdNmZ2eza9curK2t77ptVITp+vbbb/n000/59ttv67sqQtw0CZpM1Lhx47CwsCA8PJxr164Znd+8eTMtWrQgNzcXHx8flixZUu4U9qqwt7dn2LBhQOkYp+vNnDmTuXPn0qxZM1JTU8nPz2f27NlGizTqF5v08/PjwoULaouTRqNhy5Yt9OnTh8zMTE6cOMH48eN57rnnjMpq27YtBw8eZPDgwVhbW5OSkkKrVq1YtGgRYWFharqkpCROnTpFZmbmDe/Nw8ODPXv28MgjjwClA82bN2/OkiVLDPad048BS0pKqtIzW7VqFS+//DLu7u4kJiai1WoZO3YsW7ZsqfHmyitWrCAjI4MxY8ZgY2NTozyEqGvvvfcezz//vPrHmxCmTNHJyDyTNX78eFatWsWMGTNYsGAB8M84oitXrtSoa0ncnuLj42ndujVZWVmcOXNG3Z6lprKysnB0dCQqKgo7O7vaqeRdQFEU/P39iYmJkUHNVdS1a1cuXbqEh4dHuQu2ivLJd636cnJyaNOmDZmZmUY7MtQWaWkyYfPmzaNRo0a8//778sPoDvfss8+Snp7OnDlzbjpgEkIIUTMye86Eubu7s2HDBj799NNyN44Vd4633nqLhg0bqguVCiGEqHsSNJm4hx9+mIcffri+qyFuMfmchRCi/knQdIfRT3EXQgghRO2SMU1CCCGEEFUgQZMQQgghRBVI0CSEEEIIUQUSNN1hIiMjURTlhq/w8PBbVn5wcDCKovDNN9/csjLqm6Iotb4GVmho6C3/bISoD66urnh6euLq6lrfVRHipknQdAcLCAgo9+Xs7FzndenRoweKohAVFVXnZde2gICAGu/jVxFXV1ejz2bNmjUoisLUqVNrtSwh6tKWLVuIj49ny5Yt9V0VIW6azJ67Qw0ZMuSObu2pT9HR0bWe5+TJk5k8eXKt5yuEEKL2SEuTEEIIIUQVSNB0l1u1ahXt27fH1tYWDw8PgoKCOH78uEGairqJwsPDURSF0NDQG5bRo0cP9u3bB5RuuPvYY4/dMH3Z8hYtWoSnpye2trb079+f8+fPG6T19fVFURSOHTtGv379aNCgAX5+fnzyySdqmuPHjzNo0CC0Wi3W1ta0atXK4DzABx98gKIoRnUbNmwYiqKwcOFC9dj1Y5piY2NRFIU2bdqwe/du2rRpg7W1Na1bt2b37t0AfPrpp/j5+WFtbU2PHj04ceKEQTnXj2las2YNzz77LACLFy9GURQyMjK4cOECZmZmBAYGGj23AQMGoCiK+qyFEELULgma7mJbtmzh+eef57fffsPBwYH8/Hy2b99Ox44d+euvv2qtHG9vb4P3Xl5eVbpu+fLlvPLKKyiKQkFBATt37uTxxx8vd/PKvn378ttvv2FpaUlcXJw65ujMmTN06dKFbdu2UVRUhJOTE3/++ScTJkxQNzkGmDZtGp07d2bLli1EREQAsGPHDiIiImjXrh3Tpk2rtL5//fUXAwcOJCUlBY1Gw/Hjxxk8eDAffPABL7zwAkVFRZSUlLBv3z4GDhxIfn5+hXk5OjoavA8ICECj0eDj40O3bt04deoUR48eVc9nZmaye/duGjVqJCuHi9vK7NmzeeKJJ2QLIHFHkKDpDrVlyxYCAwONXvqAAErHPR08eJDTp09z6dIlUlNTee6558jLy2Pp0qW1Vpe1a9fSvXt3AP74448q511QUMC5c+eIj48nOjoaV1dX/vjjDw4ePGiUdtiwYaSkpJCens6+ffvU8t5//31ycnIYMWIEaWlpJCUl8fXXXwOlGx7rAxczMzNWr16NtbU1L730EpcvX+all17CwsKCVatWodFoKq1vUVERX375JQkJCVy+fJnu3btz9epVXn31VX755RcuXrxIXFwcAQEBXLx40eCzuN7QoUNZvXo1AFOmTCE6Ohp7e3sAxowZA8C6devU9N9++y0FBQU88cQTmJnJf2tx+4iMjGTTpk1ERkbWd1WEuGny0/UOdurUKaNXZmamQZpOnTpx7733AqDRaHj55ZcBOHv2bJ3X93pPP/00fn5+ADRt2pQnn3wSKG09ul5ISIi6pELXrl3V44cPHwZKN7y1srICSgOSdu3akZOTw8mTJ9W0AQEBzJ07l4SEBBo3bsy5c+eYMWMG999/f5Xq6+XlxeDBgwFo0KABL730EgCdO3dWN1T28PBg/PjxAJw+fbrqD6OMJ554AhsbGzZs2EBJSQkAmzdvBlCfkRBCiNonQdMdasiQIeh0OqNXcHCwmiY2Npbhw4fj7u6uBhytW7cGoLCwsJ5q/o/r13VxcXEBICMjwyit/tz1srOzAfD09DQ4rn+flZVlcHz69OlotVoKCgoAeOONN6pc3+vroH/v7u5ucFx/X1euXKly3mXZ29vz2GOPkZiYSGRkJDk5OXz//ff4+vrSqVOnGuUpxK2Ql5pKwLVrdAACrl0jLzW1vqskxE2RJQfuYiNHjuTIkSPY29sTEBAAlAZSNxprc6crKChQgzJnZ2e1JedmlDcG60bHq2LMmDGsX7+edevWkZKSQl5eHiNHjqxxfkLUpsxTpzi9fDkJO3YQVFz8v4OZ7OzSBc/+/Wk2aRKO//uZI4QpkZamu1RWVhZHjhzB19eXS5cuER0drb6up58pdn2rTF5e3i2tY3p6usF7fcvM9YOkb8TBwQGAhIQEg+Px8fEG5/VmzZplUP7MmTOrXuE61LdvXzw8PNi8ebM6tkm65sTtIPmnn4gcOpSEHTvQ6QOm/9EVF5OwYweRQ4eS/NNP9VRDIWpOgqa7lLm5OYqikJ+fT05Ojnr877//Nkqrb4X67rvvSElJAUoHPZc3kNna2hr4p1tMTz+eqGxZlfnmm2+Ii4sDICYmho0bNwJUazXujh07AjB37ly1BS0iIoKjR49iZ2dHixYt1LQ//fQTH374IU2aNCEjI4M2bdqwZMmSehvAeqNnptFoeOqpp8jIyODbb78lICCANm3a1HENhTCUeeoUhyZOpKSw0Chg0tMVF1NSWMihiRPJPHWqjmsoxM2RoOkuZWtrS69evUhKSsLLy4smTZrg5eXFgAEDjNI2b96c3r17k5ycTLNmzejVqxdNmjThzz//NEqrHzQ9adIkhg4dqh7XByeDBg2q8srXdnZ2+Pr60rhxY5o1a0ZycjLt2rXjoYceqvJ9hoSEYGdnx5dffomLiwuNGjVi2LBhAMycOVMNTHJzc3n22WdRFIXVq1fj6OjI//3f/6HRaBg3bly1gr3aon9mq1evJjAw0CgQ1c+iA6RrTtwWTi9fXhosVdb1rNOhKy7m9PLldVMxIWqJBE13sf/+978899xzuLu7k5CQgIODA//5z38qTDts2DAKCwv5448/6N+/P2+++aZRunHjxjFy5Eh0Op3BQpQhISF069aNq1evql1jlRkxYgShoaHk5+djaWnJwIED2bRpE4qiVPke7733Xg4cOMDAgQPRaDSkpaXRsmVLPvroI4N1Y0JCQoiJiWHKlCnqOkdt27bl1Vdf5fz587zyyitVLrO2tG7dmlmzZqHVajl16hTF1/3lrh+0D9I1J+pfXmpquV1yFdF31eWnpd3imglRexTdzYxGFeIW0K+GPWXKFHWFbGGsoKAAKysr7r//fo4dO3bDtPn5+QYD/LOysmjcuDHHjh3Dzs7uVlf1jqEoCn5+fpw/f/6mBvLfieK3bePIlCnVvq7Dhx/iNXDgLaiRaZPvWvXl5OTQunVrMjMzjcar1haZPSeEidJvBVOVVqb58+cTFhZmdNzX1/eW/XC5k+nXDxP/yLG1rdF1Whsb/P39a7k2dw75rlXd9ZOVbgUJmoQwQR4eHiQnJ2NlZWUwtqkis2bNYvr06ep7fUtTbGystDRVg/z1X7GMq1drdt21a8TExNRybUyffNeqry7GnkrQJIQJSktLo1mzZrzzzjtGC3eWx8rKSh30XpZ+0VNRPfLcjLl07Iii0VR5TBOAotHg2rGjPMsbkO9a1dXFc5KgSdx2goODDVYuF8ZuhxXbhSjL2tUVz/79qzwYXNFo8OzfH6sKVvMX4nYks+eEEELUimaTJqFoNFDZDFdFQdFoaDZpUt1UTIhaIkHTHaK8/djEnaWme9UJUVccAwLotGIFZhYWpcFTORSNBjMLCzqtWCFbqQiTI0HTHWDx4sW0a9dOBlPewcLCwmjXrp0Ex+K2596tGz0iIvDs398ocNJ3yfWIiMC9W7d6qqEQNSdBk4n79ddfCQkJIS0trVqLPtaX2NhYFEW57bb8iIqKYsCAATg6OmJlZUXLli1ZtmxZpdetWbMGRVHKfQ0fPlxNV1xczDvvvIOlpSWKohAVFWWQz9dff82DDz6ItbU1Tk5OPPHEE1y4cEE9n5aWRkxMDOPGjau1exbiVnEMCKB9eDj9Dx5kq6MjXwFbHR3pf/Ag7cPDpYVJmCwZCG7iZsyYQWFhIStXrpT1PGooOTmZnj17kpGRgb29Pa6urpw8eZLJkyej0WiYOHFipXk4OTnRsGFDg2NlZ7VNnDiRTz/9tNxr9+7dy+OPPw6Au7s7ubm5bNq0id9++43jx49jb2/PwoUL2bdvHxEREfz888906dLlJu5YiLph5eLCaRsbLmVm4mFjI4O+hcmTliYT9vPPP7N3717atm3LiBEj6rs6JmvJkiVkZGQwatQo0tLSSEhIYOvWrQC8/fbbVcpjzJgxREdHG7wWL16snk9LS2PcuHG0bdvW6Nq5c+cC8O9//5tLly5x+fJl+vTpQ2xsLJ9//jkAlpaWvPXWW9WqkxBCiNolQZMJW7duHYDBooWi+vQbD0+cOBELCwsAgoKC8PPzIzExsVbGES1dupRVq1aVu/r2n3/+iUajYcr/tqCwsbHhxRdfBODIkSNquqFDh+Ln58euXbtIk/26hBCizknQZMJ+/PFHFEWhX79+Bsd9fX1RFIWjR4/St29fbG1t8fHxqfY+bnl5eWi1WqysrIxmbi1YsABFUZg5c6Z6bP/+/fTv3x+tVotWq6VTp05ERERUqazQ0FAURTGq49SpU1EUhTVr1hgcj4qKIigoCK1Wi62tLT169ODo0aMGaYKDg8u99nr6oOj6gEb/vipB0y+//ELz5s2xtramdevWbNq0yeB8o0aNKry2oKDA6Jh+kbay58zMzOjXrx8lJSXs3r270joJIYSoXRI0maisrCxOnz5NYGAgrq6u5abp06cPR44cwcbGhri4OKZNm8aWLVuqXIa1tTUjRoygoKDAKPjZsGEDAGPHjgUgPj6eoKAgdu7cCYCtrS2HDx9m2LBhrFq1qia3WKGTJ0/StWtXtm/fTmFhIQ4ODuzbt49evXoZzCC85557CAgIwNHRsVbLL8+RI0eIi4vDwcGB48ePM2LECPbs2VOla5s3b05xcbEaMF67do3ly5cDxvtOde/eHYDDhw/XXuWFEEJUiQRNJury5csAeHl5VZhm1KhRZGRkkJqayrRp04DSbqLq0AdF+iAJIDo6mmPHjtG+fXuaN2+u1uP48eNERkaSkZFBYmIiq1evBmDhwoXVKrMyc+fOJTc3l3HjxpGRkcGlS5dYtmwZWVlZzJs3T003f/58oqOjGTp0aK2WX562bdty4cIFLl++TFhYGDqdTh2rVJlXXnkFgNdeew0PDw8aNmzIjz/+CMBDDz1kkFY/uFz/+QshhKg7EjSZqNTUVIAKW5mgdKyTfvr77NmzAThz5ky1yunSpQtNmzZlz549pKSkAMatTHo+Pj5qSwiUdo85Oztz9uzZapVZmcjISDQaDeHh4eoYpBdffBFvb2+1pauuBAcHo9Pp+P3339XPYubMmVhaWvLHH39UKY8nnniCxYsX07hxY3Jzc3FycgJKZ9I9+uijBmnd3NwA1M9CCCFE3ZGgyUTpg4XyxsPolQ2oXP431bfs+JyIiAgCAwPLfZU1evRoiouL1XE6GzduxNLSkieffFJNc+3aNaZPn46Pjw8ajUYN1tLT0ykqKrrp+y0rJSWF4uJiHBwcDNZFiouLIzExsd73ZbO0tMTd3Z2srCyys7OrdM3LL79MXFwc2dnZdO7cGSgdmG5ubrgqSH5+vlqGEKbg7bff5ssvv5RZn+KOIOs0mSh9i4O+xakmMjMzOXXqVKXpxowZQ2hoKBs3bqRz585ql5dLmTVXQkNDWbRoERYWFvj7+6P530rAVcm/uvSDpAMqWCAvLy9PDSrrW3V33T59+jSbNm3C0dGRqVOnGp3Xf976z1+I212vXr3w9/cnJiamTnahF+JWkqDJRLm7u2Nubn7D7rb09HS0Wi3wz75lZQdFBwcHExwcXGlZvr6+dOvWjf3797No0SLAuGtu165dQOmsthYtWhhcW3Zl64ro65mVlWVwPC8vzyitk5MT2dnZHDt2DCsrq0rzrmnZ+vf68+XZtGkTBw4cIDg4WF3lvKCggOTkZCwsLLCzs6tWXebPn09JSQkvv/xyueXqP++yC2cKIYSoG9I9Z6KsrKzo0KEDiYmJFQZOS5YsQafTodPpeO+99wBo1qxZjcobO3YsJSUlrF27FldXVwYMGGBwXt+yU3b9oPT0dJKTkw3SWVtbAxh1W+lbjTZt2kRubi4AV69eZfv27UZ16dq1K4WFhUyfPl3tnrx48SJhYWHlBlmVadmyJQArVqxQuxK3bdvG+fPn8fT0vGHQlJeXx+LFi5k5cyZXr14FSpdjKCgooH379piZVf2/WFxcHF988QX29vbltjIB6lID3WTfLiGEqHMSNJmwPn36ABitCaS3ceNGnJ2dcXV1VYMm/QKK1TV8+HBsbW0BeOqpp4y6vwYNGgSU/jL39vbG398fDw8PoyDG3d0dd3d3YmJi8PX1VYOARx55hICAAE6cOEGTJk3o3bs3/v7+pKenG9UlNDQUa2trPvroI7RaLV5eXnh7exMaGmowY23WrFkEBgZWulbUSy+9hIODA+vXr8fZ2RlPT0/1ft544w01nX4M2KxZs9RjI0aM4IEHHuD777/H3d2dhg0bMmfOHIPB91X1/vvvU1hYyEsvvYSzs7PR+ezsbHbt2oW1tbVsoyJMxokTJ/jll184ceJEfVdFiJsmQZMJGzduHBYWFoSHh3Pt2jWj85s3b6ZFixbk5ubi4+PDkiVLCAoKqlFZ9vb2DBs2DCgd43S9mTNnMnfuXJo1a0Zqair5+fnMnj0bHx8fg3T6xSb9/Py4cOGC2uKk0WjYsmULffr0ITMzkxMnTjB+/Hiee+45o7Latm3LwYMHGTx4MNbW1qSkpNCqVSsWLVpEWFiYmi4pKYlTp06RmZl5w3vz8PBgz549PPLII0DpQPPmzZuzZMkSg33n9GPAkpKS1GOWlpbs3LmT8ePH4+joSFZWFg8++CARERFGrXE3kpyczKpVq7Czs6twhfcVK1aQkZHBmDFjsLGxqXLeQtSniRMn8tBDD1VpD0chbneKTkbmmbTx48ezatUqZsyYwYIFC4B/xhFduXLlhl1LwnTEx8fTunVrsrKyOHPmDL6+vjeVX1ZWFo6OjkRFRVV73NXdTFEUGdRcTV27duXSpUt4eHhw4MCB+q6OyZDvWvXl5OTQpk0bMjMzy92yqjZIS5OJmzdvHo0aNeL999+XH0h3sGeffZb09HTmzJlz0wGTEEKImpHZcybO3d2dDRs28Omnn9KhQ4f6ro64Rd566y0aNmxY7XFSQgghao8ETXeAhx9+mIcffri+qyFuIfmMhRCi/knQdAeKjY2t7yoIIYQQdxwZ0ySEEEIIUQUSNAkhhBBCVIEETXeh0NBQFEUhPDy83PM9evRAURSioqJqLc/biaIoshSDEEKIapOgSQghhBCiCmQguBBCiFvm+++/x9fXVyaoiDuCtDQJIYS4Zezs7HBwcJCV58UdQYImUSX6cU579uxh5MiR2Nvb4+npabBBLkBxcTGvv/46bm5u2NvbM3LkSNLS0gzSpKam8vLLL+Pj44ONjQ3NmjVjxowZ5OTklFvm7t271TLd3NyYMmUKBQUFVar3J598gr+/v7rJ7e+//26UJjY2FkVRaNOmjcHxqKgoFEWhR48e6rE1a9agKApTp07lww8/xNvbGxsbG/r160dsbCwFBQVMmzYNFxcXHB0deeaZZ8jIyKj0vtzd3Xn99dcpKSnh5MmT9OzZExsbG/z8/Fi+fLl67dWrV7G3t0er1Rpthvziiy+iKAqfffZZlZ6NEEKI6pHuOVEtjz32GIqiYGNjQ2JiIn5+fgbnX3/9dYqLi/Hw8CAtLY0vv/ySgoICIiIi1DQTJkwgIiICCwsLPDw8iI2N5f333+eXX37hp59+MipzyJAhmJmZYW9vT1JSEh9++CG2trbMnz//hnXds2cPEyZMAECr1fL333/Tt2/fWngK8Omnn5KXl0ejRo0oKiri+++/Z/jw4bRp04bPP/8cNzc3EhMT+eKLLygsLGTjxo0V3leDBg1ITk5m/vz5WFlZ8dFHH1FcXIy1tTWxsbG8+OKL+Pr60r9/f2xtbXn88cf57LPP2L59O48//jgAJSUlREREYGVlxWOPPVYr9yiEEMKQtDSJarnvvvtITU3l8uXL7Nu3j9GjRxuc12g0JCcnEx8fzy+//IKVlRXffPMN8fHxapqNGzeyZcsWMjIyiIuLIyYmBi8vL/bv38/hw4eNymzZsiWpqakkJiby1VdfAbBkyRJKSkpuWNdly5YBpbusp6amkpaWxvTp02/2EQCQm5vLqVOniIuL4/Tp07i5uXH06FE2bdrExYsXiY+P5+DBg1hZWfHVV1+RlJRU4X1dunSJJUuWAKWzEJ966ilSUlJIS0tj6tSpACxevFi9bsyYMQCsW7dOPfbzzz9z6dIl+vXrh6OjY63coxC1YdWqVYSGhrJq1ar6rooQN02CJlEtb7/9NhYWFgB069bN6Pz06dNxcXEBoGPHjmrLztmzZ9U0FhYWDB48GFtbWwC8vLx44oknjNLpvfvuu1haWgIwfPhwWrRoQW5urkEgVh59XiEhIWg0GhRFYfLkydW634o888wzNGnSBAA/Pz9GjhwJlHaRNWzYEIBOnTrRp08fdDodZ86cueF9TZ48WR3zMWXKFBRFwczMTN1r7vTp0+p1PXv2xNvbm+3bt5OZmQnA5s2bAXjyySdr5f6EqC3/93//R1hYGP/3f/9X31UR4qZJ0HQXUhSlxun0AVFFXF1dy01fdlzP119/zQMPPECDBg1QFAVFUVi0aBEAhYWFlZbp5uYGwJUrV25YF31A4ezsrB6rrVaY6+ukf+/u7m5wXP88yqtrRXk4OTnd8HpFUXj66afJz89n8+bN6HQ6vv76a2xtbRk0aFBNb0kIIUQlZEzTXUjfwpOenl7u+dTUVAAaNGhQ62WfOHGCkSNHUlRUhKenJ40bNwbg1KlT1c5Lp9PVdvVuWkV1qk5dy0t7/bExY8Ywf/581q1bR8uWLbl48SIjR468JZ/ZnSI1NY/Dh1PJySnCzs6cjh1dcXW1ru9qCSFMiARNd6GAgAAA9u/fb3Tu0qVLnD17Fmtra7y9vaud9/WBmL6FRN/Cs3fvXoqKiggJCeH9999X04WGhhIWFlbt8m5EX+aVK1fUFcCvXbtmlE7fspOVlWVw/PrZabeTwMBA2rVrx969e9XxUNI1V75TpzJZvvw0O3YkUFz8T/Cp0Sj07+/JpEnNCAiQcWBCiMpJ99xdqE+fPjg7OxMZGcm7775LUVERAMnJyYwePZr8/HyGDh2qjrepjs8//1wNlI4ePcquXbsAuPfeewHU8VAZGRnqQG6dTkd0dPRN39f19GX++9//pri4GIClS5capXN0dKRhw4bExsayb98+9fiXX35Z63WqTWPGjKGkpIQvvvgCBwcH+vfvX99Vuu389FMyQ4dGGgVMAMXFOnbsSGDo0Eh++im5nmoohDAlEjTdhWxtbVm+fDkajYbZs2djZ2dH48aNueeee/jxxx/x9PRk4cKFNco7Pz+fhg0b0rhxY9q3b8+1a9d4/PHH8fLyAqBv375YW1vzySef4OLiwr333ouTk1O5U/Jvln7Q9/Lly3FxccHFxaXcoAngpZdeQqfT0bdvXx5++GHatGnDypUra71OtWnUqFHqvx977DGsrKzqsTa3n1OnMpk48RCFhSVGAZNecbGOwsISJk48xKlTmXVcQyGEqZHuubvUiBEj8PDwYMGCBRw6dIjk5GQaN27MwIEDeeutt/Dw8KhRvtOmTePChQv897//xc7OjqCgIINApWnTpmzdupW5c+fy559/kpycTLdu3fDw8Kj1Kck9evTg008/5d133yUhIYH777+fTz/9lNatWxulnTlzJhkZGaxdu5ajR4/StWtXwsLCbus1j8oOupeuOWPLl5+muFhHZcPJdDooKiohPPxv3nmn7Q3TKoqCvf1V0tLyb8sxdbejkhIboAElJTaUlOgwM6vaRBQhbkeKTv7nC2GyFEVBq9WSkpKCuXnV/wbKysrC0dGRqKioO3J7i9TUPLp02VlhC5OoH4cPD8DFRVpEq0JRFPz9/YmJiZEAvYpycnJo06YNmZmZODg43JIypKVJCBO1bds2oLTVsLKAKT8/n/z8fPW9ftC7fsmHO83hw2kSMN2G7tTv262gf07yvKquLp6VBE1CmKBOnTqpq6c///zzlaafP39+ubMTfX19b9lfZPXJ1jan8kSizvn6+uDmZlvf1TAp129VJSp2/QzoW0GCJiFMUEpKCt7e3rz22mu0a9eu0vSzZs0y2EImKyuLxo0bExsbe0d2z129mlHfVRDliI29QHa2dM9VhaIo+Pn5cf78eemeq6LrN32/FSRoEsIEnTt3rlrpraysyp1dp9Pp7sgfyB07uqDRKNXqojMzg+++642TU8W/1BVFwdfXh9jYC3fkc7sVyj4zrdZCnls13an/R2+FunhOEjQJIe44rq7W9O/vWe76TOXRL3TZtOmNuyoVRcHNzZbsbCv5RVZF8szEnUTWaRJC3JEmTWqGRqNQ2dhQRSkNmiZNalY3FRNCmCwJmu5CZTfPFaajsg2KhaGAAEdWrOiEhYUZGk35kZNGo2BhYcaKFZ1kK5VbZMKECXTu3JkJEybUd1WEuGkSNN1lFi9eTLt27YiJianvqohq+O233wgICGDLli31XRWT0q2bOxERPejf39MocNJ3yUVE9KBbN/d6quGd76+//uLQoUP89ddf9V0VIW6aBE13kV9//ZWQkBDS0tJMYu2P2NhYFEWhTZs29V0VA1FRUQwYMABHR0esrKxo2bIly5Ytq/S6/fv306RJExRFYerUqQbnYmNjGTFiBM7OzlhbW/PAAw/w9ddfq+eLi4vJyMggODiYuLi42r6lO1pAgCPh4e05eLA/ixe3Z968tixeXPo+PLy9tDAJIapMgqa7yIwZMygsLGTlypWy9kcNJScn07NnT3bs2IFOp8PV1ZWTJ08yefJkVqxYUeF1P/30Ez169Ci3ha+goIBHHnmEr776ivz8fJydnfnjjz8YPnw43377LQAdO3Zk7ty5ZGRk8Pbbb9+y+7uTubhYMXCgFyNH+jJwoJesTC2EqDYJmu4SP//8M3v37qVt27aMGDGivqtjspYsWUJGRgajRo0iLS2NhIQEtm7dCnDDYCY9PZ0WLVowadIko3Pr16/nzJkzdO7cmUuXLpGYmMgnn3yCTqfjnXfeUdNNmTKFhg0b8tlnn3Hx4sXavzkhhBA3JEHTXWLdunUABgsciur7888/AZg4cSIWFhYABAUF4efnR2JiYoWD7Lt27crvv/9Ohw4dKszz+eefx97eHoDx48fj6OjI77//TnFxMQA2NjZMmjSJwsJCNm3aVNu3JoQQohISNN0lfvzxRxRFoV+/fgbHfX19URSFo0eP0rdvX2xtbfHx8SE8PLxa+efl5aHVarGysjKa5bVgwQIURWHmzJnqsf3799O/f3+0Wi1arZZOnToRERFRpbJCQ0NRFMWojlOnTkVRFNasWWNwPCoqiqCgILRaLba2tvTo0YOjR48apAkODi732uvpg6Lrtx7Rv68oaHJ1dVWDrOsVFBSUe7ykpISSkhKKiorUYwMGDABg165dN6ynEEKI2idB010gKyuL06dPExgYiKura7lp+vTpw5EjR7CxsSEuLo5p06ZVa6aWtbU1I0aMoKCgwCj42bBhAwBjx44FID4+nqCgIHbu3AmAra0thw8fZtiwYaxataomt1ihkydP0rVrV7Zv305hYSEODg7s27ePXr16GYwvuueeewgICMDRse4HBTdv3hyAjz/+mOzsbAA++eQTsrOzueeeewxW8n7wwQdp0KCBuu+cEEKIuiNB013g8uXLAHh5eVWYZtSoUWRkZJCamsq0adMAWLp0abXK0QdF+iAJIDo6mmPHjtG+fXs1OPDy8uL48eNERkaSkZFBYmIiq1evBmDhwoXVKrMyc+fOJTc3l3HjxpGRkcGlS5dYtmwZWVlZzJs3T003f/58oqOjGTp0aK2WXxXPPPMM7u7uHDp0CA8PDxo1aqSuafPQQw8ZpNVoNHh4eHDlyhWDFighhBC3ngRNd4HU1FSACluZoHSsk6IoKIrC7NmzAThz5ky1yunSpQtNmzZlz549pKSkAMatTHo+Pj50795dfR8cHIyzszNnz56tVpmViYyMRKPREB4ernaPvfjii3h7e6stXfXNzs6OnTt30qVLF4qLiw26/q5/bgBubm7odDr1cxVCCFE3ZO+5u4A+WKho7AwYBlQuLi6A4ficiIgIZs2aVe610dHR6r9Hjx7NnDlz2LRpE5MmTWLjxo1YWlry5JNPqmmuXbvG7Nmz2bx5M/Hx8ZSUlNTovqoiJSWFkpISozFIeoWFhRWONapLbdq04cCBAwDs2LGDAQMG0KRJE/r372+UNj8/HwBLS8s6raMQNTFu3DgsLCwoLCys76oIcdMkaLoLuLm5AdxUy0RmZianTp2qNN2YMWMIDQ1l48aNdO7cWe3y0gdiUDqQe9GiRVhYWODv749GowGoUv7Vpd8gNCAgoNzzeXl5t0XQVNa7774LwOzZs9VnU1ZqaioajQYnJ6e6rpoQ1fbcc8/h7+9PTEyMbNgrTJ4ETXcBd3d3zM3Nb9jdlp6ejlarBf7Z46zsoOjg4GCCg4MrLcvX15du3bqxf/9+Fi1aBBh3MelnfkVFRdGiRQuDay9cuFBpGfp6ZmVlGRzPy8szSuvk5ER2djbHjh0zGFBdUxWVrX+vP19T+/bt48CBA/j5+TF69Gij8zk5OSQlJdGoUSOTWNVdCCHuJDKm6S5gZWVFhw4dSExMrDBwWrJkCTqdDp1Ox3vvvQdAs2Y12/V97NixlJSUsHbtWlxdXdVp8nr6lp20tDT1WHp6OsnJyQbprK2tAdQZZXr6VqNNmzaRm5sLwNWrV9m+fbtRXbp27UphYSHTp09XuycvXrxIWFhYuUFWZVq2bAnAihUr1IHY27Zt4/z583h6et500KRvZXr99dcxNzf+m+ann36iqKiIbt263VQ5Qgghqk+CprtEnz59ACpcFHHjxo04Ozvj6uqqBk1TpkypUVnDhw/H1tYWgKeeesqo+2vQoEEAdOvWDW9vb/z9/fHw8DAKYtzd3XF3dycmJgZfX192794NwCOPPEJAQAAnTpygSZMm9O7dG39/f9LT043qEhoairW1NR999BFarRYvLy+8vb0JDQ1l7ty5arpZs2YRGBhY6VpRL730Eg4ODqxfvx5nZ2c8PT3V+3njjTfUdBEREQQGBlY4Dqw8v/32Gz/88AM+Pj7lDgAH2Lx5M/DP5ynE7S4nJ4esrCxycnLquypC3DQJmu4S+sGY4eHhXLt2zej85s2badGiBbm5ufj4+LBkyRKCgoJqVJa9vT3Dhg0DSsc4XW/mzJnMnTuXZs2akZqaSn5+PrNnz8bHx8cgnX6xST8/Py5cuKC2OGk0GrZs2UKfPn3IzMzkxIkTjB8/nueee86orLZt23Lw4EEGDx6MtbU1KSkptGrVikWLFhEWFqamS0pK4tSpU2RmZt7w3jw8PNizZw+PPPIIUDrQvHnz5ixZsoSJEyeq6fRjwJKSkqr41FCXQJg1a1a546wSEhL473//i4uLC8OHD69yvkLUp0cffRRHR0ceffTR+q6KEDdN0cnIvLvG+PHjWbVqFTNmzGDBggXAP+OIrly5ctNdS+LWGjlyJF9++SXvvPOOuixETWVlZeHo6EhUVBR2dna1VMM7n6IoMqi5mrp27cqlS5fw8PBQZ4iKysl3rfpycnJo06YNmZmZFc6YvlnS0nQXmTdvHo0aNeL999+XH14m5osvvuDLL7+kVatW6uKjQggh6pYETXcRd3d3NmzYwOjRo8vdOFbcvgYNGsRjjz3Gpk2b1PFiQggh6pYsOXCXefjhh3n44YfruxqimhwcHKq8obEQQohbQ4Kmu1xsbGx9V0EIIYQwCdI9J4QQQghRBRI0CXELJSQkYGVlhaIoBAYGsnTp0lrNv0OHDtjb26MoSrnLOwghhKg9EjQJcQsVFhaqK5GfOnXqpvb/K8/p06fVRQPj4uJqNW8hhBCGJGi6TcTGxqIoSqWvqVOn1ndVRQ10794dnU5HaGgoAHv27GHgwIG0a9eOiRMnlhtMbd26lZ49e+Lk5ISDgwM9e/Zk27ZtBmkyMjI4f/58XdyCEELc9WQg+G3CwsJC3VMNSlsl9Moed3d3r9N63c4iIyPp2bMnQ4YM4Ztvvqnv6lTZuXPn6N+/v9oCdfToUc6dO6duZAzw73//m9deew0o3TjZ2tqayMhIIiMjmT9/PjNnzqyXugtRXStWrKBhw4Zcvny5vqsixE2TlqbbhKenJ9HR0epr7969AAwZMsTgeHX2MhO3p507d1JQUMDatWvJzs6mf//+7N69W918+O+//2bWrFlYW1uzceNGrly5QnJyMqtWrcLa2prZs2fz+++/1/NdCFE1rVq1onPnzrRq1aq+qyLETZOgSYg6VlhYCICXlxd2dna4uLig0+koKioC4PPPP6e4uJiXXnqJESNGqF2z48aNIyQkhJKSElauXFmftyCEEHclCZpM1IoVK2jVqhXW1tY0bNiQyZMnG+0iHhwcjKIorF+/nkmTJqHVanFycuKFF14gLy+PxMREBg8eTIMGDfD09CQ0NNRojyNFUdBqtfz222907twZa2trmjVrxvr1643qlJKSwoQJE7jnnnuwsrLivvvuY926dUbp9HlGRUXRpUsXbG1tCQwM5NtvvwVKg4oPPviAVq1aYWNjQ+PGjRk1apTB2B191xzAli1bUBSFqKgo4J/xYW3atDEoNyoqCkVR6NGjh3pszZo16lixpUuX0rhxY3Vz0bLdCVV53lXVvXt3FEWhX79+eHl58d///pcHHngAR0dHAE6ePAlAnz59jK4NDQ0lOzub8PDwGpUthBCi5mRMkwkKCwtTBxS7u7uTnZ3NsmXLiI2NNRooDPD8889TUlKCk5MTiYmJfPzxx1hZWbF7927i4+Np0KABiYmJhIWF4e7uzqRJkwyuz8zMpE+fPpibm2NpacmZM2d4+umnad68uRqYXL16lR49enDy5EnMzc1xd3fn5MmTPP3001hYWPDEE0+Um6eFhQVmZmZcvHhRHbu1fPlyXn31VRRFoVGjRmRkZLBhwwZ+/PFH/vzzT9zd3Y22EgkICMDa2rrGz3TVqlXk5eXh4eFBTk4OJSUluLm51eh5V6Zt27asXLmS+fPnc/nyZXr06MHy5cvV85mZmQA0bNjQ6FozMzPZYFeYlD179nD06FEyMzPVP3SEMFXS0mRiMjIyWLBgAebm5uzcuZNLly6RnJxMr1692L59O/v37ze6xtnZmeTkZBISEti6dSsAS5YsISAggNTUVC5fvsyiRYsAWLx4cbnlvvfee6SmppKamsqIESPQ6XQsW7ZMPb927VpOnjzJgw8+SEJCAvHx8Rw5cgQbG5sKx2G9/fbbJCUlceXKFXbv3q0GTS+//DI//vgjFy9eJD4+nsuXL9O/f39SU1NZs2YNULo+0fXjvgIDA2v2UCndHfvMmTNcvHiR06dP89lnn6EoSo2ed1U8//zzxMTEkJOTw969ew3qLjuaizvJm2++yYgRI3jzzTfruypC3DQJmkzMoUOHyMvLY+jQoTz66KMA2NnZ8cYbbwClg4yvN2fOHOzt7QEICgrivvvuA2DixIlYWFgAMGXKFOzs7Dh37hzFxcVGeTz33HMAWFpaqrO6zpw5o56PjIwEYO7cuWoLSbt27XjiiSc4d+6cQVq9F154ASidOdipUyeDc71798bT0xMAGxsbJk6cCMDZs2dv/IBq6JlnnsHX1xeAJk2a0KhRI6Bmz1sIIcSdSbrnTExKSgoAX331FYqiGJ0vby85FxeXct+XXb5AURRcXFy4cOECOTk56vgaKJ3ybm5ubnR9RkaGUb0GDhxYbr1jY2O59957DfI0Mys/Zj9+/DghISH89ttvpKenG5zTD6Kubdc/I72aPO+bVV45Qggh6p8ETSambNdN2fWb9JycnGqUV1WOVyUvS0tL/Pz8jM5XFCBdr6CggKCgIC5evIiTk5N6j2XXrapLtfm8q0ofsOoDtrJKSkq4evUqGo0GGxubWi9bCCFExSRoMjH6X9KjR49m7dq1dVJmZmYmOp1ObQG5cuUKgEFrlL5eO3bsoFevXjUuKzo6mosXL9K5c2d++ukntYWr7Gy5yujrkpWVZXA8Ly+v2vWpj+fdokULtmzZwo8//kjfvn0Nzi1cuJAZM2YwYcIEWXZACCHqmIxpMjEdO3bEwsKCL7/8kt27d6vHd+zYUe70/try2WefAf8sBwDQrFkz9XzXrl2B0kGfycnJAOTm5vLuu+9y4cKFKpejH2N19epVgyDn77//NkprZWUFYDT139HRkYYNGxIbG8u+ffvU419++WWV66FXH8/7mWeeQaPR8OGHH/Lll1+qrV2bN28mNDQURVGYMGHCLSlbCCFExaSlycQ0bNiQV155hQULFtCnTx8aNmxIfn4+mZmZaDQaWrduTcuWLWu1TAsLC0JCQggJCSE/P5/s7GzMzMyYPHmymmb8+PEsX76cgwcP4uXlxT333ENSUhJFRUVs27aNgwcPVqmse++9l4CAAI4dO4abmxuenp7k5uZy6dKlctNaWFiwe/du7r33XrZu3arOQnvppZd488036du3Lx07diQ7O7vcweiVqY/n3aJFC959911mzJjByJEjmTBhAubm5qSlpQEwb948HnzwwVotU4japispoeDKFWxKSmgA2JSUkP+/77ConKIoXLW3Jz8tTWbUVlHB/3ZVuJUkaDJB8+fPx9fXlxUrVhAdHY2lpSUDBgzg9ddfr/Vf4AC2trZEREQwefJk/v77bwICAnj77bdp3bq1msbe3p4DBw7w1ltvsW3bNi5dukSjRo14/PHHef3116tclrm5Odu2beP111/nwIEDJCQk0LJlSyZNmsScOXMM0rq6uvKf//yHd955h7Nnzxq0TM2cOZOMjAzWrl3L0aNH6dq1K2FhYTz22GPVvv+6ft4Ar732GoGBgfznP/8hKiqKgoICunfvTkhISIWD7YW4nRRcucJ3HTvyov7A5ct817FjfVZJiJum6CSEFTegKAqOjo4GM+VE1cXGxuLn50f37t3VZRnqo4z8/Hzy8/PV91lZWTRu3Jhjx47JYpnVoCgKfn5+nD9/Xv76r0R+WhrbO3So72qIu0wIpeNwHRwcbkn+0tIkRB3Yt28fgYGBTJ482aBb82Z16NCB48ePV5pu/vz5hIWFGR339fW9ZT9c7mTlzRAVhq7+b204Ie4kEjQJUUdOnTpFampqreZ5+vRpgxakisyaNYvp06er7/UtTbGxsdLSVA3S0lR1Mn5J3IkkaBLiFvL19b2lv1yr2m1qZWWlzjYsS6fTyS//GpDnVjkLrZYBhw+jKAo+vr5ciI2VZ1YN8tyqLzc3F25iyZuqkKBJ3JD8ZxVC1IRiZoaViwuKomDr5oZVdrb8PKkGeW7VV1jOH4a1TdZpEkIIIYSoAmlpMlEZGRlotdr6roaoQ1euXLkl27YIcSvNnz9f3VFg5syZ9V0dIW6KtDSZoMWLF9OuXTtiYmLquyqijoSFhdGuXTtZ+kGYnG3btrFq1Sq2bdtW31UR4qZJ0GRifv31V0JCQkhLS1P3grudxcbGoigKbdq0qe+qGIiKimLAgAE4OjpiZWVFy5YtWbZsWbXzCQkJQVEU/v3vf5d7/pdffqFnz544Ojri5eXFk08+abStjK+vL4qilPsKDg4GIC0tjZiYGMaNG1ftOgohhKgd0j1nYmbMmEFhYSErV66UtWJqKDk5mZ49e5KRkYG9vT2urq6cPHmSyZMno9FomDhxYpXyycrK4uOPP8be3r7cveB+++03unfvTmFhIR4eHmRmZrJx40YOHz7Mn3/+SYMGDQzS33vvvZiZlf4dk56eTkpKChqNBijdqHffvn1ERETw888/06VLl5t8CkIIIapLWppMyM8//8zevXtp27YtI0aMqO/qmKwlS5aQkZHBqFGjSEtLIyEhga1btwLw9ttvVzmflStXkpWVxfjx43F0dDQ6rw9wFy1aRFJSEpcvX6ZXr17ExsayadMmo/RHjhwhOjqa6Oho+vfvD5RuGAxgaWnJW2+9Ve06CiGEqD0SNJmQdevWARgsUiiq788//wRg4sSJWFhYABAUFISfnx+JiYlVGjdUWFjI4sWLMTc3Z8qUKUbni4uL8fb2Zty4cbz88ssA2NjYqMHuqVOn1LS+vr40adJEbVUqKSlhx44dAAwYMEBNN3ToUPz8/Ni1a5e6ea8QQoi6I0GTCfnxxx9RFIV+/foZHNePiTl69Ch9+/bF1tYWHx8fwsPDq5V/Xl4eWq0WKysrrly5YnBuwYIFRrNf9u/fT//+/dFqtWi1Wjp16kRERESVygoNDUVRFKM6Tp06FUVRWLNmjcHxqKgogoKC0Gq12Nra0qNHD44ePWqQJjg4uNxrr6cPiq7fPkT/vipB07p160hISGD48OH4+PgYnddoNKxevZpVq1apXW4A165dA0o3ONaLjIzk7Nmz6rFff/2VlJQUWrVqhZeXl5rOzMyMfv36UVJSwu7duyutoxBCiNolQZOJyMrK4vTp0wQGBuLq6lpumj59+nDkyBFsbGyIi4tj2rRpbNmypcplWFtbM2LECAoKCoyCnw0bNgAwduxYAOLj4wkKCmLnzp0A2NracvjwYYYNG8aqVatqcosVOnnyJF27dmX79u0UFhbi4ODAvn376NWrl8EMwnvuuYeAgIByu8pq28KFCwF49dVXgdLA58CBAze8JiYmhk8++QRra2tGjhxZYbrt27cDMHDgQKNz3bt3B+Dw4cM1qrcQQoiak6DJRFy+fBnAoOXheqNGjSIjI4PU1FSmTZsGwNKlS6tVjj4o0gdJANHR0Rw7doz27dvTvHlztR7Hjx8nMjKSjIwMEhMTWb16NfBPQFFb5s6dS25uLuPGjSMjI4NLly6xbNkysrKymDdvnppu/vz5REdHM3To0Fot/3o7duzgzz//pHv37jz44IMA9OzZk6CgoHLTZ2RkoCgKTZo0IS8vj507d+Lv719h/vqgqWzXnJ6npyfwz/dBCCFE3ZGgyUToN3qtqJUJSsc66aeqz549G4AzZ85Uq5wuXbrQtGlT9uzZQ0pKCmDcyqTn4+OjtnxAafeYs7MzZ8+erVaZlYmMjESj0RAeHq6OQXrxxRfx9vZWW7rqkn55AX0rU2U0Go3aAhYXF8fnn39OYWFhuWmTkpL4448/0Gq1PPTQQ0bn3dzcANTPRgghRN2RoMlE6IOFgoKCCtOUDahcXFwAw/E5ERERBAYGlvsqa/To0RQXF6szvDZu3IilpSVPPvmkmubatWtMnz4dHx8fNBqNGqylp6dTVFR00/dbVkpKCsXFxTg4OBisYRQXF0diYmKFAcit8Ntvv7F3714CAwPL7T4rj729PdHR0aSmpvL000+zatUqlixZUm7a7777Dp1Ox6OPPqoODC8rPz8fKJ1NJ4Qp6NGjB8OHD6dHjx71XRUhbpqs02Qi9C0M+hanmsjMzDSYtVWRMWPGEBoaysaNG+ncubPa5aUPxKB0IPeiRYuwsLDA399f/QVflfyrS79ZZUBAQLnn8/Ly1KDyVtO3Mulb9arD3NycadOm8dlnn7Fjx45yZ0HeqGsO/vn89d8HIW538+bNw9/fn5iYGNl4Vpg8CZpMhLu7O+bm5jfsbktPT1f3o9PPfis7KDo4OFhdYfpGfH196datG/v372fRokWAcdfcrl27gNJZbS1atDC49voVr8ujr2dWVpbB8by8PKO0Tk5OZGdnc+zYMaxqYRfrisrWv7/Rnn4//PADABMmTDBa0DIzMxOtVktGRgYXLlzg888/x8/Pj6efflpNY25e+l+uvBl6BQUF7Nq1q9wZknr6z18/tkkIIUTdke45E2FlZUWHDh1ITEysMHBasmQJOp0OnU7He++9B0CzZs1qVN7YsWMpKSlh7dq1uLq6GrV86Ft2yq4XlJ6eTnJyskE6a2trALKzsw2O61uNNm3aRG5uLgBXr15VW1rK6tq1K4WFhUyfPl3tnrx48SJhYWHlBlmVadmyJQArVqxQuxK3bdvG+fPn8fT0vGHQ1KZNGx588EGjl17btm0BaNCgAW+//TYTJkxQZ7oVFRWp3XL33nuvUd4//fQTOTk5tG/fnoYNG5Zbvn6pgW7dulXzroUQQtwsCZpMSJ8+fQDKXU0aSsceOTs74+rqqgZN5S28WBXDhw/H1tYWgKeeesqo+2vQoEFA6S9vb29v/P398fDwMApi3N3dcXd3JyYmBl9fX/WX/iOPPEJAQAAnTpygSZMm9O7dG39/f9LT043qEhoairW1NR999BFarRYvLy+8vb0JDQ1l7ty5arpZs2YRGBhY6VpRL730Eg4ODqxfvx5nZ2c8PT3V+3njjTfUdPoxYLNmzVKP7d27l99++83oBaWtenv37gVKx5e9+uqrXL16lU6dOtG4cWNcXV1ZuXIlFhYWTJ061ahelXXNZWdns2vXLqytrWUbFSGEqAcSNJmQcePGYWFhQXh4uLpIYlmbN2+mRYsW5Obm4uPjw5IlSyqcBl8Ze3t7hg0bBpSOcbrezJkzmTt3Ls2aNSM1NZX8/Hxmz55ttNCjfrFJPz8/Lly4oLY4aTQatmzZQp8+fcjMzOTEiROMHz+e5557zqistm3bcvDgQQYPHoy1tbW68OOiRYsICwtT0yUlJXHq1CkyMzNveG8eHh7s2bOHRx55BCgdaN68eXOWLFlisO+cfgxYUlJSFZ+aoXfeeYcVK1bQunVrUlNTsba2pl+/fhw8eJAOHToYpa8saFqxYgUZGRmMGTMGGxubGtVJiLo2ZMgQvLy8GDJkSH1XRYibpuhkZJ5JGT9+PKtWrWLGjBksWLAA+Gcc0ZUrV27YtSRMV3x8PK1btyYrK4szZ87g6+t7U/llZWXh6OhIVFQUdnZ2tVPJu4CiKDKouZq6du3KpUuX8PDwqHQBWPEP+a5VX05ODm3atCEzM9Nox4faIi1NJmbevHk0atSI999/X34A3UWeffZZ0tPTmTNnzk0HTEIIIWpGZs+ZGHd3dzZs2MCnn35abhePuDO99dZbNGzYUF20VAghRN2ToMkEPfzwwzz88MP1XQ1Rh+QzF0KI+idB0x0gNja2vqsghBBC3PFkTJMQQgghRBVI0CREFY0ZMwZFUbC3t6d37971XR2gdLHLwMBAdT++ytaoEkIIUXMSNAlRRXFxcUDptNZz587Vc21KZWdnG+z3V9kaVUIIIWpOgqZbKDIyUm0BKPuyt7enW7du6v5t9cnX1xdFUcrdC60yoaGhKIpCeHh4rderPMHBwSiKwjfffFMn5VXk/Pnzt804ssceewydTsecOXPquypCCHHHk6CpjgQEBKivBg0asH//fvr161fvAUCTJk0ICAhAo9FU+1pXV1cCAgJwdnZWj61ZswZFUcrdJqSqevTogaIoREVFGRy/5557CAgIwN7evsZ5CyHq1owZM/jkk0+YMWNGfVdFiJsms+fqwJAhQ4yCo5UrVzJx4kSmTZvGkCFDUBSlXuqm3wuuJiZPnszkyZNrsTY3Nn/+fObPn19n5Qkhbt7gwYNlZWtxx5CWpnrywgsvEBgYSGxsLGfOnKnv6gghhBCiEhI01SNvb28A0tLSAMOuraVLl9K4cWMcHR159NFHuXz5snrdihUraNWqFdbW1jRs2JDJkyeTk5NjlH90dDQjRozAzc0NKysrmjZtyqxZs8jKylLTlDemSVEUtFotv/32G507d8ba2ppmzZqxfv16g/yvH9O0Zs0ann32WQAWL15slO+JEycYPnw4rq6u2NnZ0bZtWz799FODvz579OjBvn37gNKNeh977DH1XEVjmvbt20fPnj2xs7PDxsaGjh07Gs0ii42NRVEU2rRpw+7du2nTpg3W1ta0bNnyprpIy35mH374Id7e3tjY2NCvXz9iY2MpKChg2rRpuLi44OjoyDPPPGM0fkzfHbl7925GjhyJvb097u7uvP7665SUlHDy5El69uyJjY0Nfn5+LF++vMb1FUIIUXPSPVdPiouLiY6OBkrH6pS1atUq8vLy8PDwICcnh5KSEtzc3AAICwsjNDQUKN1SJTs7m2XLlhEbG8u2bdvUPM6dO0enTp3IzMzE0tISNzc3zp07x4IFC/j7778rDRQyMzPp06cP5ubmWFpacubMGZ5++mmaN29OmzZtyr3G0dHR4H3ZsVI5OTkEBQURFxdHgwYNcHJy4tixYzz//PNcuHCBt99+G/gnkNTz8vK6YT33799Pnz59KCoqwtHRERsbG44cOcKwYcNYt24do0aNMkj/119/MWDAAFxdXTE3N+fkyZMMHz6c48eP06JFixuWdSOffvopeXl5NGrUiKKiIr7//nuGDx9OmzZt+Pzzz3FzcyMxMZEvvviCwsJCNm7caJTHkCFDMDMzo0GDBiQnJzN//nysrKz46KOPKC4uxtramtjYWF588UV8fX3p379/jesrRF2JiYnh2rVrJCUl4efnV9/VEeKmSEtTPcjIyGDGjBnExcVx//33G23AmpOTw5kzZ7h48SKnT5/ms88+U1ttFixYgLm5OTt37uTSpUskJyfTq1cvtm/fzv79+9U83nvvPTIzMxk8eDCpqanEx8dz4cIFnnvuuSq3VLz33nukpqaSmprKiBEj0Ol0LFu2rML0Q4cOZfXq1QBMmTKF6OhoddC2nZ0dR48eZceOHWRlZXHx4kV27dqFRqNh8eLFFBUVAbB27Vq6d+8OwB9//MHSpUtvWMe5c+dSVFTEtGnTSE9PJyUlhUWLFgGl+7Vdr6ioiK+++oqEhATS0tJ4/PHHKS4uZsmSJVV6JhXJzc3l1KlTxMXFcfr0adzc3Dh69CibNm3i4sWLxMfHc/DgQaysrPjqq69ISkoyyqNly5akpqZy6dIltT6hoaE89dRTpKSkkJaWpg6wX7x48U3VV4i6Mnr0aO677z5Gjx5d31UR4qZJ0FQHtmzZYrDkgJOTEx988AG2trblBjDPPPOMGkg1adKERo0aAXDo0CHy8vIYOnQojz76KFAajLzxxhsA7Ny5U83j0KFDAMybN08NXLy9vfn000+NWrYq8txzzwFgaWnJa6+9BnBT469cXV3p168fZmalX7vevXtz//33k52dTXJyco3yPHz4MIqiMHfuXDXfqVOn4ubmxtmzZ7ly5YpBei8vLwYPHgyAlZWVOqOn7FpHNfHMM8/QpEkTAPz8/Bg5ciQAL774Ig0bNgSgU6dO9OnTB51OV+5zfPfdd7G0tARKB9nb2dkBpQGooiiYmZmpG/aePn36puorhBCi+qR7ro4EBASo/7azs6NNmzZMnz693C4hFxeXcvNISUkB4Kuvvip3tl3ZtYOys7OByru3KuLo6Ii5+T9fD32darKeE0BJSQnvvvsuq1evJi4uTm1Z0issLKxRvtnZ2Tg6OqoBhl6jRo1ISUkhKysLJycn9fj1z1bf7Xl9cFVd1+erf+/u7m5w3NXVtcLyyssjJyfHoP43ul6I25FtcTGtgYZXrxK/fTuuHTti/b/vsRCmRoKmOlDekgM1UXbAdNkgTK/sL9fbzcqVK3nzzTcxMzPD19cXCwsL4OZbeGrLrZoKXVG+1SmvvLQydVvc7jJPneL08uW8kJJS2qWRlcWvU6agaDR49u9Ps0mTcCzn55gQtzMJmkyIPigaPXo0a9euvWFaBwcHoHTrD61WW+2yMjMz0el0aouWvmXj+sHeVaVf/Xzr1q0MGDBAPV52tlxNODg4kJWVRU5OjtrapNPpSExMVM8LIepW8k8/cWjiRHTFxUZjQHTFxSTs2EHiDz/QacUK3Lt1q5c6ClETMqbJhHTs2BELCwu+/PJLg0Upd+zYwbp16wzSdu7cGYA33nhD7aqLjY3lySefJCEhoUrlffbZZ0Bp19kHH3wAQLNmzW54jZWVFYDREgj6liX98goA165d48KFC1XOozwdO3ZEp9Px1ltvUVJSApQOkk5JSaFp06a3deubEHeizFOnODRxIiWFheiKi8tNoysupqSwkEMTJ5J5m7Q2C1EVEjSZkIYNG/LKK6+Qn59Pnz59cHd3R6vVMmDAAMaMGcNff/2lpg0JCcHR0ZGtW7fi5uaGr68vTZs2ZePGjUyaNKnSsiwsLAgJCcHNzQ0XFxe++OILzMzMKl0BXD9Ga/Xq1QQGBqoB26BBgwAYM2YMjRo1okmTJjg7O5e7h5s+j0GDBlVa3ptvvom5uTmLFi3CxcUFNzc3pk2bBqAuzSCEqDunly8vDZYq60LW6dAVF3Na1h0TJkSCJhMzf/58VqxYQZs2bcjKykKn0zFgwAD27dtHy5Yt1XRNmjTh0KFDDB8+HDs7OxITE/H29ubVV1+ttGsPwNbWloiICDw9PcnPzycgIIANGzbQunXrG17XunVrZs2ahVar5dSpUxT/7y/NZ555hqVLl3LfffeRlZVFVlYWzz33HA899JBRHiEhIXTr1o2rV68SHx9/w/Iefvhhdu3aRffu3SkoKCAnJ4d27drx1Vdf8fTTT1d6n0KI2pOXmkrCjh0VtjBdT99Vl1+mBVqI25mikxGl4jqKouDo6FjjmXJ3Kv34q/PnzxutrVXfQkNDCQsLY/Xq1QQHBxudz8/PJz8/X32flZVF48aNOXbsmNHMQ1ExRVHw8/Pj/PnzMhi/HPHbtnFkypRqX9fhww/xGjjwFtTIdMl3rfpycnJo3bo1mZmZt2w8qwwEF6KaAgMD6dKly01tdlxbdu/ezb/+9a9KZyHOnz+fsLAwo+O+vr4yWL4GZGXr8uXY2tboOq2NDf7+/rVcmzuDfNeqruwWYbeKBE1CVFN+fj7nzp2r72oApetUVWXZhlmzZjF9+nT1vb6lKTY2VlqaqkH++r+xjKtXa3bdtWvExMTUcm1Mm3zXqq8qk4dulgRNQlRRZGRkfVfByGOPPValH6hWVlbqrMSydDqd/ECuAXlu5XPp2BFFo6nymCYARaPB9X+zYIUx+a5VXV08JxkILozodDoZzySEqDZrV1c8+/dH+d9G3ZXRL3RpVcEuCELcbiRoEkIIUWuaTZpUGjSVs9WTAUVB0WhoVoUlUIS4XUjQZIKkFejuU1RUVCf99ULcLMeAADqtWIGZhUWFLU6KRoOZhQWdVqyQrVSESZGgycQsXryYdu3ayaDJu8i1a9cYMmQIY8eOre+qCFEl7t260SMiAs/+/Sm57py+S65HRIRsoSJMjgRNJuTXX38lJCSEtLQ0dU+421lsbCyKotCmTZv6roqBqKgoBgwYgKOjI1ZWVrRs2ZJly5ZV6dq0tDQmTZqEv78/dnZ2tG3bljVr1pQ7ADEuLo6nn36axo0b4+joSOfOndm6dWu5+W7evJn27dtjb2+Pj48PY8aMURf21Gg0XL58ma+//poPP/yw5jcuRB1yDAigfXg4K9zc+Bz4wcGB9osX0//gQdqHh0sLkzBJEjSZkBkzZlBYWMjKlStl7Y4aSk5OpmfPnuzYsQOdToerqysnT55k8uTJrFix4obX5ubm0qlTJ1asWEFiYiJarZaoqCieffZZ3njjDYO0iYmJPPjgg6xbt4709HRsbW05dOgQgwcP5uOPPzZI+/nnnzN8+HB+++03HBwcSEtL4/PPP6dDhw5kZGRgaWnJxo0bsbW1JTQ0VN2aRghTcE2j4ThwwtYWr4EDZdC3MGkSNJmIn3/+mb1799K2bVtGjBhR39UxWUuWLCEjI4NRo0aRlpZGQkKC2vrz9ttv3/Da9evXc/bsWe6//37i4+OJj4/nu+++w8zMjA8++IDc3Fw17UcffURqaip9+vTh8uXLJCUlqUHZ3Llz1XQlJSW88sorKIrC5s2bSUhIUAO7pKQk1q9fD4C/vz/jxo3jypUrVW4VE0IIUbskaDIR69atAzBYoFBU359//gnAxIkTsbCwACAoKAg/Pz8SExNvOMhev4jkc889h6urKwD9+/endevW5OfnG2w+rE/70ksv0aBBAwBeeOEFnJycSEhIUAOsmJgYmjVrxujRoxk2bBgADRo0UP99/vx5Nc9XXnkFQA2khBBC1C0JmkzEjz/+iKIo9OvXz+C4r68viqJw9OhR+vbti62tLT4+PoSHh1cr/7y8PLRaLVZWVly5csXg3IIFC1AUhZkzZ6rH9u/fT//+/dFqtWi1Wjp16kRERESVygoNDUVRFKM6Tp06FUVRWLNmjcHxqKgogoKC0Gq12Nra0qNHD44ePWqQJjg4uNxrr6cPiq7fOkT//kZBk1arBeDqdase6wMg/fmK0hYVFZGfn4+VlRU2NjYANG3alAMHDvDZZ58Z5BkdHQ1Ao0aN1GO+vr40b96cEydOkJycfIO7FEIIcStI0GQCsrKyOH36NIGBgWoLx/X69OnDkSNHsLGxIS4ujmnTprFly5Yql2Ftbc2IESMoKCgwCn42bNgAoM7eio+PJygoiJ07dwJga2vL4cOHGTZsGKtWrarJLVbo5MmTdO3ale3bt1NYWIiDgwP79u2jV69eBjMI77nnHgICAnB0dKzV8ssaOXIk1tbWLFmyhCNHjpCTk8PChQs5ffo0ffv2xdPTU007ZswYFEXhnXfe4e+//yYzM5OQkBCuXr3KmDFjMDOr+L9eUlISn332GdbW1gwfPtzgXPfu3dHpdBw5cuSW3acQQojySdBkAi5fvgyAl5dXhWlGjRpFRkYGqampTJs2DYClS5dWqxx9UKQPkqC0xePYsWO0b9+e5s2bq/U4fvw4kZGRZGRkkJiYyOrVqwFYuHBhtcqszNy5c8nNzWXcuHFkZGRw6dIlli1bRlZWFvPmzVPTzZ8/n+joaIYOHVqr5ZfVtGlT1qxZQ2JiIh07dsTe3p6QkBCaNGnCf//7X4O0Dz/8MAsWLOCvv/6iRYsWaLVawsPD6dSpE4sWLaqwjJKSEsaOHUtOTg7Tpk0z+sz1gZn+OyGEEKLuSNBkAlJTUwEqbGWC0rFOiqKgKAqzZ88G4MyZM9Uqp0uXLjRt2pQ9e/aQkpICGLcy6fn4+NC9e3f1fXBwMM7Ozpw9e7ZaZVYmMjISjUZDeHi4OgbpxRdfxNvbW23pqiuZmZmEhoYCYGNjg5eXF4qicO7cOaPB2RcuXFCDIzs7O+655x4Ajhw5YhCUXu+dd95h165dtG7dmjlz5hidd3NzA1A/HyGEEHVHgiYToA8WCgoKKkxTNqBy+d+U3rLjcyIiIggMDCz3Vdbo0aMpLi5m06ZNAGzcuBFLS0uefPJJNc21a9eYPn06Pj4+aDQaNVhLT0+nqKjopu+3rJSUFIqLi3FwcFDLURSFuLg4EhMTKSwsrNXybmTevHlER0fzxBNPkJqaysWLFzl27Bhubm68/fbb/P3332rakJAQLl26xJQpU7hy5QqJiYns3bsXS0tLXn75ZdLT043y37t3L2FhYdja2rJ+/fpyN9jNz88HwNLS8tbdqBC1yNfXlxYtWuDr61vfVRHippnXdwVE5fStC/oWp5rIzMxUZ3TdyJgxYwgNDWXjxo107txZ7fJyKbO2SmhoKIsWLcLCwgJ/f380/9sqoSr5V5d+0ciAChbCy8vLU4PKW+2HH34ASoMnW1tbAFq1asXzzz/Pu+++yw8//KB2Yf7www9oNBoWLFiAuXnpf7MePXowbNgw1q1bx/79+xkyZIia96VLlxg1ahQlJSUsXrxYzed6+u+A/jshxO3uiy++wN/fn5iYmDrZhV6IW0mCJhPg7u6Oubn5Dbvb0tPT1Rlb+tlvZQdFBwcHExwcXGlZvr6+dOvWjf3796vdS9d3ze3atQsondXWokULg2svXLhQaRn6emZlZRkcz8vLM0rr5OREdnY2x44dK7flpboqKlv/vuwMuOvpW/quH8StX51d3wqkT6tvFassbXFxMU899RTJyckMHz6c8ePHV1gH/Xeg7KBzIYQQdUO650yAlZUVHTp0IDExscLAacmSJeh0OnQ6He+99x4AzZo1q1F5Y8eOpaSkhLVr1+Lq6sqAAQMMzutbdtLS0tRj6enpRtPgra2tAYxWsNa3Gm3atEmdrn/16lW2b99uVJeuXbtSWFjI9OnT1aDl4sWLhIWFlRtkVaZly5YArFixQu1K3LZtG+fPn8fT0/OGQVPbtm0BePPNN7l27RpQuu6TfoXvBx980CBtUVERs2fPVsuJjIxk8+bNKIrCAw88oKYNDQ1l7969+Pj48Mknn9yw/vouvk6dOlXzzoUQQtwsCZpMRJ8+fQDUsUbX27hxI87Ozri6uqpB05QpU2pU1vDhw9Xup6eeesqo+2vQoEEAdOvWDW9vb/z9/fHw8DAKYtzd3XF3dycmJgZfX192794NwCOPPEJAQAAnTpygSZMm9O7dG39//3LH+YSGhmJtbc1HH32EVqvFy8sLb29vQkNDDVbWnjVrFoGBgZWuFfXSSy/h4ODA+vXrcXZ2xtPTU72fsluh6MeAzZo1Sz02e/Zs7O3tWb9+PS4uLjRu3Jj777+flJQUHn30UXr16qWmfeeddzA3N+eDDz5Qy+nZsyd5eXk8//zzNG3aFCjtlnv33XcByMnJoVOnTgbjzcqWv3//fpKTk+ncubP6+QghhKg7EjSZiHHjxmFhYUF4eLjaylHW5s2badGiBbm5ufj4+LBkyRKCgoJqVJa9vb26IvWYMWOMzs+cOZO5c+fSrFkzUlNTyc/PZ/bs2fj4+Bik0y826efnx4ULF9QWJ41Gw5YtW+jTpw+ZmZmcOHGC8ePH89xzzxmV1bZtWw4ePMjgwYOxtrYmJSWFVq1asWjRIsLCwtR0SUlJnDp1iszMzBvem4eHB3v27OGRRx4BSgeaN2/enCVLljBx4kQ1nX4MWFJSknqsRYsWHDlyhCeeeIIGDRpw+fJlmjZtSmhoKFu2bDHoiuvZs6e6AKi5uTnp6em0atWKxYsXs3z5cjVdXl4eJSWl+8CnpaVx6tQpg1fZ8hcsWACUriwuhKmYNm0ajz76qLoUihCmTNHJyDyTMX78eFatWsWMGTPUX6D6cURXrly5YdeSMG3bt28nKCiIgIAATp48ecPFMasiKysLR0dHoqKisLOzq6Va3vkURZFBzdXUtWtXLl26hIeHBwcOHKjv6pgM+a5VX05ODm3atCEzM9No14faIi1NJmTevHk0atSI999/X3743EUyMzMJDg5Go9GwYsWKmw6YhBBC1IzMnjMh7u7ubNiwgU8//ZQOHTrUd3VEHXF0dOTNN98kPz+fHj161Hd1hBDiriVBk4l5+OGHefjhh+u7GqKOvfzyy/VdBSGEuOtJ0GTiYmNj67sKQgghxF1BBkcIIYQQQlSBBE1CCCGEEFVQraApMjISRVF47LHHajUtlE6vrO0p86GhoSiKQnh4eK3mW5tiY2NRFIU2bdpUmvbjjz/G3t4eRVH45ptv1OM1uc/8/Hy8vLwwMzPjr7/+qn7F/ycmJoYJEybg7++PlZUV9vb2dOzYkffff7/SNZPqQ3WetxBCCFHWLWtpsrW1JSAgAC8vL/WY/MKquXnz5vHCCy+Qk5NTK/l9/PHHJCQk8Nhjj6lbi1TXsWPHaNu2LZ988gkXLlzAzc2NBg0acOTIEWbMmMGDDz6obn0CpRvWKopCVFRUrdyDEEIIUZdu2UDwDh06EB0dfauyv+ukpKQwePBg8vPz+f77728qr7y8PHVxzNdff73G+bz22mtkZWUxYsQIlixZQsOGDQE4efIk8+bNo0ePHlhaWt5UXYUQpm3kyJGYmZmpK98LYcpk9pyJeO2112jUqBHBwcE3ndfKlStJTEzk0UcfpV27djXO5+eff8bMzIxPPvnEYPXVFi1a8MUXX9x0PYUQpu/ll1+Wla3FHeOWdc9dP6YpNjYWPz8/oLRb5/pxOXp//PEHXbt2xcbGBn9/f3UH+bKKi4uZP38+zZo1w9LSEjc3N0aNGlWt6fcRERF07NgRGxsb7Ozs6NmzJ/v27TNK99dff9GrVy9sbGzw9vZm8eLFBAcHG9R/3LhxKIrChg0bDK49cuQIiqLQs2fPKtdLb+zYsSiKogZJjRo1qtb1CQkJNGrUCAsLC3bt2qUez8vLUzf0nT17drXrdT1FUar0g7BHjx7q823btq3BWDf981y/fj2TJk1Cq9Xi5OTECy+8QF5eHomJiQwePJgGDRrg6elJaGholcrU6XS88847eHl5YWtry6OPPsr58+eN0lU0/u6bb74x+Azgn/Fj//nPf3jzzTdp2LAh9vb2jBgxgrS0NLKyshgzZgwODg64urry0ksvkZ+fb5Cvr6+v2k3Zr18/bG1t1e8WlAaj7du3x9ramubNm7N582b12gsXLmBmZkZgYKDRfQwYMABFUcr9HgshhLh5ddbSZGFhgZWVlfoLJCAgAHt7e4M0mZmZ9O7dGwsLC6ytrTl//jwvvPACPj4+PProo2q6F198kY8//hhFUWjUqBGpqals2LCB/fv3c+zYMVxcXG5Yl/Xr1/PUU08B4OrqSmFhIZGRkfTp04c9e/aoi0cWFhYyePBgYmJisLCwoKSkhKlTpxrlN2bMGFavXs0XX3zBk08+qR7X/7IbOXJktZ7Ve++9x9q1a+nduzeffPJJta4FuHbtGkOGDCEpKYlVq1bRt29f9dzKlStJSkqqlUUy27dvT2RkJIMGDSI0NJSHHnoIa2vrctN6e3sbvC871k3v+eefp6SkBCcnJxITE/n444+xsrJi9+7dxMfH06BBAxITEwkLC8Pd3Z1JkybdsH6rV6/mzTffBMDNzY2DBw8aPIub8dZbb1FYWEjDhg1JS0vjq6++ori4mKysLA4ePIijoyNJSUksXboUW1tbNVAtq0+fPuh0Omxtbbl48SJTp06lpKSE2bNn4+DggIWFBdHR0YwYMYJjx45x33334ePjQ7du3di3bx9Hjx7lwQcfBEr/7+zevZtGjRrJ4qdCCHGL1NmSA56enuoYp9atWxMdHU3v3r2N0s2bN4/k5GTS0tLUVZDLzgq7ePEin376KVZWVhw8eJD4+HguX75Mt27dSEhIYMWKFZXW5a233gJg0aJFpKSkkJ6ezrRp0ygqKmLu3Llqup07dxITE4Ofnx/nz58nPj6eP//80yi/7t274+Pjw/fff096erp6fPPmzZibmzN8+PCqPSRg27ZtzJo1i/vuu4/NmzdjYWFR5Wv1xo4dy9GjR3nzzTcZN26cwbklS5YApS0WDg4OeHl5ERYWVqPxBh988AHOzs7s37+f3r174+joSOfOnZk5cyZnzpwxSLt27Vq6d+8OlLYmLl261Cg/Z2dnkpOTSUhIYOvWrWp9AwICSE1N5fLlyyxatAhAbZW5kWXLlgHw7rvvcvnyZTIzM3nkkUeqfZ/lKSws5OLFi1y8eJEjR45gZWXF119/TUJCAsnJySQmJvL1118DsHTpUoqLi43yGDp0KCkpKaSkpPDqq68CMH36dObNm8elS5dIS0vjiSeeoKSkxOB5jRkzBoB169apx7799lsKCgp44oknZG86IYS4RW67n64vvPACAGZmZrzxxhsAnDp1Sj3/66+/UlJSwqBBg+jUqRMADg4OvPbaa0Bpl9iNpKenc/bsWdzc3NRWIzMzMzVYKnv92bNnAXjmmWfw9PQEoGXLlgwYMMAgT0VReOaZZygsLOSrr74CICoqinPnztG7d29cXV2rdO9//fUXgwYNQqfTsX37dhwdHat0XVlvvfUWX331FX5+fgYBIMAvv/zCuXPnALh8+TIWFhYkJCQQGhrK8uXLq13WAw88wJkzZ1i8eDEDBw7E1dWVQ4cO8d577xEQEMC8efOqld+cOXPU1segoCDuu+8+ACZOnKgGj1OmTMHOzo5z586VG4iUdfbsWTQajRqQmJmZERYWVt3bLNdrr72mDnx/8MEH1RasqVOnYmdnB5QGRS1btuTq1askJCQY5TFz5kzMzMxQFIWZM2eqx1966SUALC0t1e/16dOn1fNPPPEENjY2bNiwQQ129a2aZVs6hbgddOnSBUVR6NKlS31XRYibdlsFTY6OjgZ/Jbu5uQFw5coV9Vh2djaAGsTo6d9nZWXdsAz99dePEbKzs8PR0dHgev06Q87OzgZp9fUqS//Xv34A9KZNm4Cq/xJLTU1l0KBB6vtLly5V6bqyNm/ezNtvvw2AlZWVUVDx888/A6UzGy9fvkxaWpo6i65sy82RI0cIDAws93X9L39nZ2defvlltm3bRkJCArGxsbz77ruYm5vz5ptvVmsG5fXdqvr37u7u6jFFUXBxcaGoqKjS5ReysrKws7MzaK0r77OriarUFVAD5rLf4fLy0P9bq9Vibv5Pr3l519vb2/PYY4+RmJhIZGQkOTk5fP/99/j6+qp/SIjak5qax/bt8WzcGMu2bfEkJ+fWd5WEEPXktgqaKmIKMy6aNWtGx44dOXDgABcvXmTz5s1YWVkxdOjQKl2fkJBAXFwcISEhAEyaNKnaXWYHDhygefPmDBkyhOjoaLUrTk8/UP75559XW3RCQkKwtbXl/PnzFBUVAXD16lVOnTpV7quwsPCGdfDx8WHWrFmMGTMGnU6nBmo3o6LP/3b8XtxsXat6fdkuuu3bt5OXl1ftsXPixk6dymTq1F/p0mUnU6b8yuzZfzBlyhG8vFYwZcoRTp26/RZvFULcWiYRNJWln9p+fYtHfHy8wfnKrk9MTDT4RZSTk0NmZqbB9frusetbCVJSUsrNe/To0eh0Ot544w2io6Pp169ftbrYli5dyvvvv8+oUaP4/fffa9Rl9t133/Hxxx/j6OjInDlzSEpKUs/pA6WyayeZmZlhZWVFUVGRGqT16NEDnU5X7svX11e99urVqxXWQ1EUoH4DGwcHB3JycgwCvfI+O/1K9Ne3Uubl5d3S+t2Mvn374uHhwebNm9WxTdI1V3t++imZoUMj2bEjgeJiw+9wUZGOHTsSGDo0kp9+Sq6nGgoh6kOdBk1WVlYAN7Wqdfv27TEzM2Pr1q0cOnQIKP1l9/777wOlXU96+plc+i45ACcnJ+69915SUlLULqmSkhJ1cHjZ6++9916gdBCzPkiLjo5m586d5dbtySefxNLSkrVr1wLVnzU3ceJEABYuXIidnR1vvPEGyclV/6H89NNP4+vrS8OGDZk7dy5ZWVlMnz5dPX///fcDpbPKcnNLuxg+//xzrly5QvPmzau1EGV8fDzNmjVj2rRpBtP4i4uLWbduHZ9//rnROIba+Pyr495776W4uJiFCxcCpZ/zhx9+aJSuSZMmmJubc+jQIXX8nE6nU8en3Y40Gg1PPfUUGRkZfPvttwQEBMhK+7Xk1KlMJk48RGFhiVHApFdcrKOwsISJEw9Ji5MQd5EaBU1btmypcMzLrFmzKrzO3d0dZ2dnzp07h6+vL7t376522V5eXjz//PPk5+fz0EMP4eXlhbu7O/v37+eee+5RB5LDP0HCO++8YxAM6QcDT5s2DTc3N1xcXFi0aBEajUadog7Qr18/7r33XnWNKS8vL1q0aME999xTbt1cXFwMBokPHjy4yvelryuUjrd66623yMjIULvrqqLsQpX/+te/uP/++9mwYQN79uwBSgcQN2/enMjISNzd3fH09FS7ecoORK6K48ePk5ubS3h4OP7+/jRs2BB/f3/s7e15+umnyc/PZ/r06TRv3ly9pkWLFgAMGjSIyZMnV6u8mtAPqH799ddxc3NDq9Wqz6Ise3t7xo4dy7Vr13jggQfo0aMHzZs3rzA4vl3oPzuofoAuKrZ8+WmKi3VU1kiq00FRUQnh4X+TlpYvrwpeJSU2QANKSmwoKbn9utSFqI4ar9NUdkZbWWW7g65nZmbGxx9/zLRp07hw4YJBC1B1LFu2DG9vb1avXk1sbCwODg4MHjyYBQsWGMxUGzBgAJMnT2bt2rUGs49GjRqFlZUVCxYs4MSJE5iZmdGtWzdCQ0Pp1q2bms7c3JwtW7bw0ksv8fPPP6PT6Vi6dClHjhzhs88+K3dqd+fOnfnmm28YMWIEDRo0qPI96buz9KZOncr//d//8fnnnzN+/HiDelWFRqNh6dKldOvWjX/9618cO3YMS0tLvv/+e0JCQvjhhx9ISUmhVatWhISEMHr06GrlP2DAAP7++29WrVrFli1biI2N5eLFizg6OvLwww/z/PPPGy21EBISwu+//86hQ4fU7tRbacyYMSQkJPDRRx+RlpbGQw89xJw5c9SlD8patGgRJSUlREREcPToUfr378+MGTOMlmy4nbRu3Vr9t3TN1Y7U1Lxyu+QqUlICu3YlsWtXxT/3xIsAXL4MV64U4OJiVc/1EaLmFN3tOJr2Nte7d2/27NnDTz/9ZLSQ4Ny5c5kzZw5ff/11lQeBC1ETBQUFWFlZcf/993Ps2LEbps3PzzdYmTwrK4vGjRtz7NgxdYkEAdu2xTNlyo2XLRE1d+TIQAmaqkhRFHWNQPk1XTU5OTm0bt3aaHxybZK95yqxePFiLl26xDvvvINGo+Gbb74hMjISW1tb2rZta5A2MzOTzz//HAcHB6O1nISobfrV4qvSyjR//vxy16jy9fW9ZT9cTJGtbd2Mt7tb+fr64OZmW9/VMCn67cdE5Spbcqg2SNB0A2lpaYSGhpKRkcHKlSuxsLDg8uXLQOm+bWX/Qv/www8JCQmhoKCAf/3rX+qgZyFuBQ8PD5KTk7GysjIY21SRWbNmGUwK0Lc0xcbGSktTGVevZtR3Fe5osbEXyM6Wn41VIS1N1VcXk4wkaLoBFxcXDh48yMyZM9m3bx+ZmZm0adOGyZMn89xzzxmkvXLlCtbW1jz22GPVXglbiOpKS0ujWbNmvPPOO0YLvZbHysqq3EBev5SEKNWxowsajVLlMU0AZmbw3Xe9cXKSYKA8v/9+FCcnZ65cSUertZDvWzXJ/9Gqq4vnJGOahLgLZWVl4ejoSFRUlLQ0XWfq1F+rPBhco1Ho39+T8PD2dVAz06QoCv7+/sTExMgv/2qQ51Z9OTk5tGnT5paOaTK5xS2FEOJWmjSpGRqNwnUTWo0oSmnQNGlSs7qpmBCi3knQdIfIyMio7yqIW6gqe+2J2hEQ4MiKFZ2wsDBDoyk/ctJoFCwszFixohMBAdXfWFsIYZokaLoDLF68mHbt2hETE1PfVRG3wLVr1xgyZAhjx46t76rcNbp1cyciogf9+3saBU7m5goDBngSEdGDbt3cK8hB6B06dIjvv/9e3cFBCFMmQZOJ+/XXXwkJCSEtLc1ogczbUWxsLIqi3HZbfkRFRTFgwAAcHR2xsrKiZcuWLFu2rNLr1qxZg6Io5b7KLu5ZXFzMO++8g6WlJYqiEBUVBfzzPCp6rVmzBo1Gw+XLl/n666/L3QZG3BoBAY6Eh7fn4MH+LF7cnnnz2vLhhx1ISJhIeHgHaWGqoldeeYV+/frxyiuv1HdVhLhpMnvOxM2YMYPCwkJWrlwp63nUUHJyMj179iQjIwN7e3tcXV05efIkkydPRqPRqHsC3oiTkxMNGzY0OFZ2VtvEiRP59NNPK7zeysrKYDPk+Ph4cnNz0Wg0WFpasnHjRlq1akVoaCjPPvusuvmyuPVcXKwYONALKB2c27BhA6SnVIi7k7Q0mbCff/6ZvXv30rZtW0aMGFHf1TFZS5YsISMjg1GjRpGWlkZCQgJbt24F4O23365SHmPGjCE6Otrgpd8QGkqXCBg3bpzRgqh6gYGBBte6uLgA0LFjRwD8/f0ZN24cV65cqVILmBBCiNonQZMJW7duHYDBooWi+v7880+gtDXIwsICgKCgIPz8/EhMTKyVQfZLly5l1apVRtNgLSwsaNKkCd7e3gb1iYuLo0mTJjRr9s/MLH33xvr162+6PkIIIapPgiYT9uOPP6IoCv369TM47uvri6IoHD16lL59+2Jra4uPjw/h4eHVyj8vLw+tVouVlRVXrlwxOLdgwQIURWHmzJnqsf3799O/f3+0Wi1arZZOnToRERFRpbJCQ0NRFMWojlOnTlXH9pQVFRVFUFAQWq0WW1tbevTowdGjRw3SBAcHl3vt9fRB0fUBjf59VYKmX375hebNm2NtbU3r1q3ZtGmTwflGjRqVe52npydnz57l22+/VY9t374dwGgrHl9fX5o3b86JEydITk6utE5CCCFqlwRNJiorK4vTp08TGBiIq6truWn69OnDkSNHsLGxIS4ujmnTprFly5Yql2Ftbc2IESMoKCgwCn42bNgAoM7oio+PJygoiJ07dwJga2vL4cOHGTZsGKtWrarJLVbo5MmTdO3ale3bt1NYWIiDgwP79u2jV69eBjMI77nnHgICAnB0vPUDdo8cOUJcXBwODg4cP36cESNGsGfPnhrlpQ+aBg4caHSue/fu6HQ6jhyRTWWFEKKuSdBkovR74Hl5eVWYZtSoUWRkZJCamsq0adOA0m6i6tAHRfogCSA6Oppjx47Rvn17mjdvrtbj+PHjREZGkpGRQWJiIqtXrwZg4cKF1SqzMnPnziU3N5dx48aRkZHBpUuXWLZsGVlZWQZb2MyfP5/o6GiGDh1aq+WXp23btly4cIHLly8TFhaGTqdj7ty51c7nypUrHDx4UG09u55+cLn+8xdCCFF3JGgyUampqQAVtjJB6Vgn/dT12bNnA3DmzJlqldOlSxeaNm3Knj17SElJAYxbmfR8fHzo3r27+j44OBhnZ2fOnj1brTIrExkZiUajITw8XB2D9OKLL+Lt7a22dNWV4OBgdDodv//+u/pZzJw5E0tLS/74449q5/f9999TXFxMr169yt0rzs3NDUD9LIQQQtQdCZpMlD5YKCgoqDBN2YBKPxur7PiciIgIAgMDy32VNXr0aIqLi9VxOhs3bsTS0pInn3xSTXPt2jWmT5+Oj48PGo1GDdbS09MpKiq66fstKyUlheLiYhwcHAzWNIqLiyMxMZHCwsJaLa+6LC0tcXd3Jysri+zs7Gpde6OuOYD8/Hy1DCGEEHVL1mkyUfoWB32LU01kZmZy6tSpStONGTOG0NBQNm7cSOfOndUuL30gBqUDuRctWoSFhQX+/v5oNBqAKuVfXfrNKwMCAso9n5eXpwaV9a06G22WlJSoLWXXDwLX03/e+s9fCCFE3ZGgyUS5u7tjbm5+w+629PR0tFotgDr7reyg6ODgYIKDgysty9fXl27durF//34WLVoEGHfN7dq1Cyid1daiRQuDay9cuFBpGfp6ZmVlGRzPy8szSuvk5ER2djbHjh0rtwuruioqW/9ef748mzZt4sCBAwQHB6urnBcUFJCcnIyFhQV2dnZVrsfhw4dJTU2lZcuWBksQlKX/vMsunCnE7eznn3/G39+fmJiYav0RIcTtSLrnTJSVlRUdOnQgMTGxwsBpyZIl6HQ6dDod7733HoDBuj/VMXbsWEpKSli7di2urq5GLSH6lp20tDT1WHp6utHUeGtrawCjbit9q9GmTZvIzc0F4OrVq2p3VVldu3alsLCQ6dOnq92TFy9eJCwsrNwgqzItW7YEYMWKFWpX4rZt2zh//jyenp43DJry8vJYvHgxM2fO5OrVq0DpcgwFBQW0b98eM7Oq/xerrGsOYO/evVhaWtKpU6cq5yuEEKJ2SNBkwvr06QNgtCaQ3saNG3F2dsbV1VUNmqZMmVKjsoYPH46trS0ATz31lFH316BBgwDo1q0b3t7e+Pv74+HhYRTEuLu74+7uTkxMDL6+vuzevRuARx55hICAAE6cOEGTJk3o3bs3/v7+pKenG9UlNDQUa2trPvroI7RaLV5eXnh7exMa+v/t3XdcVfX/wPHX5QKXvREZshyoaLly75UaWVqZo9T8WlppuUdmomma9QtnOTLLzFEq8nWXA0fmFkeI5EBAkCl7yDi/P/jeE9d7gQsiiH6ej8d9FOd8zufzOedeuW8+019jxtrMmTNp2LBhmWtFjR8/HisrKzZv3oydnR2urq7y/Xz66adyOvUYsJkzZ8rHBg0aRIsWLThw4ABOTk7UqlWLOXPmaAy+11dJ6zOpHT9+nLi4ONq1aye/F4IgCELVEUFTDTZq1CiMjIxYsmQJ2dnZWue3b99O48aNyczMxMPDg+XLl+Pn51ehsiwtLRk4cCBQNMbpYTNmzGDevHk0aNCAxMREcnNzmTVrFh4eHhrp1ItNenl5cefOHbnFSalUEhQURM+ePUlNTeXKlSuMHj2a//znP1plNW/enJMnT9K/f39MTExISEigadOmBAQEMHfuXDldbGws169fJzU1tdR7q127NocPH6Z3795A0UDzRo0asXz5co1959RjwGJjY+VjxsbG7N+/n9GjR2NtbU1aWhotW7YkMDCwxOBHl7t37xISEoK1tTUdOnTQmWbRokUAjBkzRu98BUEQhMqjkEQnc402evRo1q1bx/Tp0+UvVfU4ovv375fatSTUHHv27MHPzw8fHx9CQ0PL1e2nS1paGtbW1oSEhJRr3NWzTqFQiPE55bR8+XIMDAwoLCxk/Pjx1V2dGkN81sovIyODZs2akZqaqrXDQ2URLU013IIFC3BxcWHx4sWcOHGiuqsjPAapqamMHDkSpVLJqlWrHjlgEoSqtHXrVgICAti6dWt1V0UQHpmYPVfDOTk5sWXLFr7//ntat25d3dURHgNra2tmz55Nbm6uzlXCBUEQhKohgqanQKdOnejUqVN1V0N4jD766KPqroIgCMIzTwRNT6GIiIjqroIgCIIgPHXE4AhBEARBEAQ9iKBJEARBEARBD09N0BQcHIxCoeDVV1+t1LRQNPWzsqfu+/v7o1AoWLJkSaXm+zgkJyczefJk6tati4mJCa6urowYMaLULVyeVOV97wVBEARB7akJmsrDzMwMHx8f3Nzc5GMREREoFAp5/zChyL1792jRogXffPMNt2/fxsHBgfj4eDZs2ECrVq04e/ZsdVdREARBEKrEMxk0tW7dmrCwMFasWFHdVXnijR8/njt37tCrVy9iY2OJjo4mNTWVsWPHkpaWprVxryAIgiA8rcTsOaFEqampBAUFYWlpyebNm7G3tweKWupWrFjBiRMnuHr1KqdOnRIbyAqCoFPr1q3JycmRN+sWhJrsmWxpenhcS0REBF5eXgBcunQJhULBzp07ta67ePEiHTt2xNTUFG9vb9asWaOVpqCggIULF9KgQQOMjY1xdHRkyJAh5VoGIDAwkDZt2mBqaoqFhQXdunXj6NGjWun+/vtvunfvjqmpKe7u7ixdupSRI0dq1H/UqFEoFAq2bNmice2ZM2dQKBR069atxHrcvHmTvLw8mjVrJgdMakqlUr42LCxM73tTKywsZNmyZTRt2hQTExNsbGzo3bu3vIGv2vHjx+nbty82NjbY2NjQtm3bMjfgVXvw4AETJkzA0dERS0tL3nzzTRITE7XS/fjjjygUCiZMmKBxfMmSJSgUCvz9/eVj6ue7efNm3n//fWxsbLC1tWXMmDHk5OQQExND//79MTc3x9XVFX9/f60tENRj5C5evEiHDh0wMTGhfv368nsUFBRE48aNMTExoVWrVhrv/bFjx1AoFPTp00frPho3boxCoRBLTghPlICAAA4cOEBAQEB1V0UQHploaQKMjIxQqVTk5uYC4OPjg6WlpUaa1NRUevTogZGRESYmJty+fZsxY8bg4eHBiy++KKf74IMPWLNmDQqFAhcXFxITE9myZQvHjx/n0qVLWsHHwzZv3szQoUMBcHBwIC8vj+DgYHr27Mnhw4flRSzz8vLo378/t27dwsjIiMLCQq0vfSjaXHf9+vX88ssvDB48WD6+fft2AN58880S66LeTNfOzk7nefXxsjbE1eXjjz+Wu0ednJzIyMjgjz/+4ODBg5w7d44WLVoQHR2Nn5+fvE+amZkZp0+fZuDAgXz//fc6N/Mtbv78+SxduhQo2pT3v//9L7/++mu566rLu+++S2FhIba2tsTExLBmzRpUKhWHDh0iOjoac3NzYmJimDt3Lk5OTrz//vsa16emptKzZ08MDQ1RqVTcuHGDoUOHkpiYyIQJE3ByckKhUHD+/Hn8/Py4desWjo6OdOrUCS8vLw4ePEh8fDy1atUC4Nq1a1y7do02bdrg6elZKfcoCIIgaHomW5oe5urqKreWPP/884SFhdGjRw+tdAsWLCAuLo6kpCR5hebis9+ioqL4/vvvUalUnDx5kujoaOLj4+ncuTN3795l1apVZdbls88+A4r+OktISCA5OZmJEyeSn5/PvHnz5HT79+/n1q1beHl5cfv2baKjo7l69apWfl26dMHDw4MDBw6QnJwsH9++fTuGhoa8/vrrJdZF300iy7uZZHR0NN9++y1GRkb8/vvv3Lt3j9TUVFavXs3y5ctp0aIFAG5ubly+fJng4GBSUlKIiYlh/fr1AHz99ddllvPtt98C8MsvvxAbG0tKSgq+vr7lqmtJ7OzsiIuL4+7du+zatQso2pjUx8eHxMRE4uPj5b+s1YHbw/z9/YmLiyMxMZHBgwcjSRLjx4/nt99+4+7du8TFxdGpUycyMjJYt24dUNRK9dZbb1FQUKCxl9e2bdsANAJjQRAEoXKJoKkcxowZA4CBgQGffvopANevX5fPnz17lsLCQl5++WV5jI+VlRXTpk0DirrESpOcnMyNGzdwdHSUW40MDAzkYKn49Tdu3ADgrbfewtXVFQBfX1/69eunkaf6SzYvL4/ffvsNgJCQEG7evEmPHj1wcHAo/4N4RGfOnKGwsJD+/fvTq1cvoKi777333uPDDz/USOvh4UGXLl3kn0eOHImdnZ18/yW5f/8+SUlJ1KlTR265U6lUzJkzp1LuYc6cOXJrpJ+fH02aNAFg7NixGBkZAUWtaRYWFty8eZOCggKtPNT3amRkxJQpUwBwdnZmwIABQNFnRx2ch4eHy9cNHz4cgE2bNsnHtm/fjoGBAYMGDaqU+xMEQRC0iaBJT9bW1hq7yzs6OgJFX85q6u4sdRCjpv45LS2t1DLU17u4uGgct7CwwNraWuN6dZfYw11n6noVp/6S/eWXXwD9WyUUCoXO41lZWSWmCwwMpGHDhjpfaur7LL7kgy7Z2dlMmjQJDw8PlEolCoUChUJBcnIy+fn5pV5bnudTEQ93s6p/dnJyko8pFArs7e3Jz88nIyNDI/3Dnydd1wNyUFv8c1avXj3atWvHqVOnuHXrFjdv3uTSpUt06tRJ67MjCNVt2LBh+Pr6MmzYsOquiiA8MjGm6RGVt2uqOjRo0IA2bdpw4sQJoqKi2L59OyqVSm7RKImVlRWARrdeQkICL7zwAu+99548qNra2lo+n5qaqtH69ij8/f0JCAjAyMgIb29vlEolQKXl/ziU9Hl41K7Oh48PHz6cv/76i02bNsktW89i11xiYg6nTyeSkZGPhYUhbdo44OAgZmk9SSIiIrh37x61a9eu7qoIwiMTQVMlUgcZd+/e1TgeHR2tcb6s62NiYpAkSW7BycjIIDU1VeN6daBSvAUCioIaXd5++21Onz7Np59+SlhYGK+88opGsKNL3bp1MTIyIiQkhOTkZOzs7MjNzcXBwYFZs2bJ6Ro1aiT//8iRIxk5cqRe9xkZGVlquj/++AMo6k5s3LixfNzT05M7d+6Uem15no96tfeHWwJzcnJKLaM6vfnmm3z88cds2rQJCwuLMsenPW2uX0/lu+/C2bfvLgUF/waUSqWCvn1def/9Bvj4lP75FgRBKC/RPfc/KpUKQKsbpTxeeOEFDAwM2LVrF6dOnQKKvogXL14MFK1XoqZes0TdVQVga2tL/fr1SUhIkAcPFxYWyoPDi19fv359ADZs2CAHaWFhYezfv19n3QYPHoyxsTEbNmwASp81p2ZlZcWAAQNIT09n2LBhxMfH4+bmJgczaubm5mXmVVzr1q1RKpXs3r1bzku9BMH//d//yenULShJSUnyseTkZOLi4sosw9bWFnt7eyIjI+WxP3l5eaxcuVIrrY+PDwB79+6Vg6r8/Hy9lzaoDra2tvj5+XHt2jXOnj1L9+7dq2V8WnU4diyOAQOCtQImgIICiX377jJgQDDHjpX9OREEQSiPpy5oCgoKKnFMzcyZM0u8zsnJCTs7O27evImnp6fWekH6cHNz49133yU3N5f27dvj5uaGk5MTx48fx9nZWR5IDvDcc88BRdPiiwdDc+fOBWDixIk4Ojpib29PQEAASqWS2bNny+n69OlD/fr15TWm3NzcaNy4Mc7OzjrrZm9vrzFIvH///nrd07Jly/Dy8mL//v3Url2bOnXqaI27adu2rc51rUri6urK+++/T15eHr1796Z27do4Ojry8ccfM3XqVM6fPw/Ayy+/DEDnzp1xd3fH29ub2rVr690CNH78eKBoTIWzszPW1tY698tr1KgRPXr0IC4ujgYNGtC9e3fq1q2rczbik0Q9Vg2ena6569dTGTv2FHl5hVoBk1pBgUReXiFjx57i+vXyL4chCIJQkqeye66kMS+xsbElXmNgYMCaNWuYOHEid+7c0WgBKo+VK1fi7u7O+vXriYiIwMrKiv79+7No0SKNloB+/foxbtw4NmzYoDEzasiQIahUKhYtWsSVK1cwMDCgc+fO+Pv707lzZzmdoaEhQUFBjB8/nj///BNJklixYgVnzpzhp59+0hhkrNauXTt27tzJoEGD9G4dcnJy4ty5c8yfP5+dO3cSExODo6MjPXr0YPbs2aSkpDB06NASg7WSLF26lHr16vH9998THh6OiYkJ3bt3Z+rUqbRs2RKAGTNmIEkSGzduJCoqCltbW2bNmsX69evL7J4D+OSTT0hNTWXjxo2kpaXRp08fhg8frnMs18aNG/nwww85cOAAFy9e5M0338TT07PUQLu6FQ+Cyxqf9rT47rtwCgokyhoiJkmQn1/IkiXXmD+/eaWVr1AosLTMIikpt0aMZ3wSFBaaAuYUFppSWChhYKB7gokg1AQKSfzLf6r06NGDw4cPc+zYMXkhTLV58+YxZ84cduzYUalfsvn5+RgaPpXx9xMtNjYWFxcX+vfvT1BQULmuVS8YGhISgoWFxWOqYeVKTMyhQ4f9JbYwCU++06f7YW+vqu5q1AgKhQJvb29u3bolAnQ9ZWRk0KxZM60xwJVJfNPVYEuXLuXevXvMnz8fpVLJzp07CQ4OxszMjObNNf+6Tk1N5eeff8bKykprLadHJQKmqidJkrx4pj5dc7m5ufKK9/DvoHf1Mg41wenTSSJgquFq0uetuqmfk3he+quKZyW+7WqopKQk/P39SUlJYfXq1RgZGREfHw/ArFmzNFoPli1bxtSpU3nw4AEffvihPOhdqJnu3r3L888/T1JSEo6OjrzyyitlXrNw4UJ5vFxxnp6ej+0vsspmZlbxSRrCk8HT0wNHR7PqrkaNot4XVShbWWshVgYRNNVQ9vb2nDx5khkzZnD06FFSU1Np1qwZ48aN09qT7f79+5iYmPDqq6+yYMGCaqqxUFny8vJIT0/n+eefZ+nSpZiZlf0lNHPmTCZNmiT/nJaWRp06dYiIiKgx3XNZWSnVXQXhEUVE3CE9XfzRpg+FQiFvkyW65/TzKLPf9SXGNAnCM+hZGdNkYAB79/bA1rZyvqgVCgWenh5ERNwRX2R62rkzEBMTE3JycnjnnTfFQHA9iTFN5SfGNAmCIPyPg4MJffu66lyfSRf1Qpf16lXeL0+FQoGjoxnp6SrxRaan0aOHiC9/4anx1K3TJAjC0+v99xugVCooa7ynQlEUNL3/foOqqZggCM8EETTVYCkpKdVdBaEKPbwlzLPIx8eaVavaYmRkgFKpO3JSKhUYGRmwalVbsZWKIAiVSgRNNdTSpUtp1aoVt27dqu6qCFXghx9+wMfHR69FPZ92nTs7ERjYlb59XbUCJ3WXXGBgVzp3diohB6EqxcfHEx0dLc/uFYSaTARNNdDZs2eZOnUqSUlJNWINj4iICBQKBc2aNavuqmgICQmhX79+WFtbo1Kp8PX11bk3XUWv9ff3l9el0fVS69q1a4lpunbtChS1KiYkJPD6669TUFBQac+gpvLxsWbJkhc4ebIvS5e+wIIFzVm6tOjnJUteEC1MT5ABAwZQp06dZ2bVeuHpJgaC10DTp08nLy+P1atXizU8KiguLo5u3bqRkpKCpaUlDg4OhIaGMm7cOJRKJWPHjq20a2vVqoWtrS0ABQUF3LhxQ+c2N56envIaWunp6cTExKBUKgGYNGkSf/zxB/v372fLli0MGzassh5FjWZvr+Kll9yquxqCIDwjREtTDfPnn39y5MgRmjdvzqBBg6q7OjXW8uXLSUlJYciQISQlJXH37l127doFwOeff16p186cOZOwsDDCwsJYs2YNAG3atNFKFxgYKKcbPXq0Vjr1GlsLFiwQs5AEQRCqgQiaaphNmzYBaCxUKJTf1atXARg7dixGRkYA+Pn54eXlRUxMTKmD7PW91s7Ojrp162JjYyNfu2fPHkBzs11XV1fq1q2rsVK7rnQtWrSgS5cuXLt2jZCQkIrduCAIglBhImiqYQ4ePIhCoaBPnz4axz09PVEoFJw/f55evXphZmaGh4cHS5YsKVf+OTk52NjYoFKptGZrLVq0CIVCwYwZM+Rjx48fp2/fvtjY2GBjY0Pbtm0JDAzUqyz1mJ+H6zhhwgQUCgU//vijxvGQkBD8/PywsbHBzMyMrl27cv78eY00I0eO1Hntw9SBzcMLoKl/Li1o0vfajz76iBs3bjBy5Eg5ja5g6JdffuHGjRs0atQIgHv37nHhwgVsbW1p166dRhnq6/74449S708QBEGofCJoqkHS0tIIDw+nYcOGODg46EzTs2dPzpw5g6mpKZGRkUycOJGgoCC9yzAxMWHQoEE8ePBAK/jZsmULACNGjAAgOjoaPz8/9u/fD4CZmRmnT59m4MCBrFu3riK3WKLQ0FA6duzInj17yMvLw8rKiqNHj9K9e3eNGYTOzs74+Phgbf3kDQS+desWYWFhODs7a22oXNzevXuRJIkXX3xRHtOk1qVLFwBOnz79WOsqCIIgaBNBUw2inrLr5lbywNchQ4aQkpJCYmIiEydOBGDFihXlKkcdFKmDJICwsDAuXbrECy+8ILeIuLm5cfnyZYKDg0lJSSEmJob169cD8PXXX5erzLLMmzePzMxMRo0aRUpKCvfu3WPlypWkpaVp7Ke3cOFCwsLCnsiZOupWpr59+5Y661FXa5Saq6srgJi+LQiCUA1E0FSDJCYmApTYygRFY53U09VnzZoFwD///FOucjp06EC9evU4fPgwCQkJgHYrk5qHh4fc+gFF3WN2dnbcuHGjXGWWJTg4GKVSyZIlS+RxRB988AHu7u5yS9eTrrRgSC0vL48//vgDAwMDrS5YAEdHRwD5fREEQRCqjgiaahB1sPDgwYMS0xQPqOzt7QHN8TmBgYE0bNhQ56u4t99+m4KCArZt2wbA1q1bMTY2ZvDgwXKa7OxsJk2ahIeHB0qlUg7WkpOTyc/Pf+T7LS4hIYGCggKsrKw01jGKjIwkJiaGvLy8Si2vsmVmZhIcHIyRkRG9evUqMd2xY8dIT0/nhRdekAOk4nJzcwEwNjZ+bHUVBEEQdBPrNNUg6i9RdYtTRaSmpnL9+vUy0w0fPhx/f3+2bt1Ku3bt5C4vdSAGRQO5AwICMDIywtvbWx5/o0/+5aWeYu/j46PzfE5OjhxUPokOHTpEbm4u3bp1K3X37bJao9Tvva6AShAEQXi8RNBUgzg5OWFoaFhqd1tycrI8xV09+634oOiRI0dqzOYqiaenJ507d+b48eMEBAQA2l1z6hlcISEhNG7cWONafbb7UNczLS1N43hOTo5WWltbW9LT07l06ZLG1PyKKqls9c/FlwmojGv16ZrTJ536vVePbRKEJ93PP/+Ms7MzsbGx1V0VQXhkonuuBlGpVLRu3ZqYmJgSA6fly5cjSRKSJPHll18C0KBBxXZ6HzFiBIWFhWzYsAEHBwetL3J1y05SUpJ8LDk5mbi4OI10JiYmQNEq18WpW422bdtGZmYmAFlZWXLgUFzHjh3Jy8tj0qRJcvdkVFQUc+fO1RlklcXX1xeAVatWyV2Ju3fv5vbt27i6upYaNFXk2r179wLw0ksvlZjvjRs3CA8Px8nJiZYtW+pMc+jQIQA6d+5c+g0KwhPC29sbX19fvL29q7sqgvDIRNBUw/Ts2RNAHmv0sK1bt2JnZ4eDg4McNH388ccVKuv111/HzMwMgKFDh2p1f7388stA0Re4u7s73t7e1K5dWyuIcXJywsnJiVu3buHp6Sl/8ffu3RsfHx+uXLlC3bp16dGjB97e3iQnJ2vVxd/fHxMTE7799ltsbGxwc3PD3d0df39/5s2bJ6ebOXMmDRs2LHOtqPHjx2NlZcXmzZuxs7PD1dVVvp9PP/1UTqceAzZz5sxyX6t26dIloqOj8fT0lGce6lLW7LrCwkJ27twJQI8ePUq9P0EQBKHyiaCphhk1ahRGRkYsWbKE7OxsrfPbt2+ncePGZGZm4uHhwfLly/Hz86tQWZaWlgwcOBAoGuP0sBkzZjBv3jwaNGhAYmIiubm5zJo1Cw8PD4106sUmvby8uHPnjtzipFQqCQoKomfPnqSmpnLlyhVGjx7Nf/7zH62ymjdvzsmTJ+nfvz8mJiYkJCTQtGlTAgICmDt3rpwuNjaW69evk5qaWuq91a5dm8OHD9O7d2+gaKB5o0aNWL58ucbeceoxYMW7FvS9Vq2yuua2b9/OP//8Q+/evcWeg4IgCNVAIYlNrGqc0aNHs27dOqZPn86iRYuAf8cR3b9/v9SuJaFmSk9Pp3nz5ty8eZMTJ07QoUOHR8ovLS0NHx9rDh78A3Nz80qq5dNPoVDg4eHBnTt3xP5/evr9998xNzcnMzNT/kNDKJv4rJVfZmYmTZr0IjU1tdQJN49CDASvgRYsWMC+fftYvHgxfn5+dOzYsbqrJDxmkyZN4ubNm/znP/955IBJbeFCcHZehqGh+DWgr6JuU0vs7NLFF5merK33kZ2dg7W1CXZ2l6q7OjWG+KyVn5VV5S51o4v4bVkDOTk5sWXLFr7//ntat25d3dURqsDEiROJj48v9+rupUlMhPx8axQKseaTvoq+yKwpKDAUX2R6Sk5WkJkJ5uYKCgpsq7s6NYb4rJVffn7JaxhWFhE01VCdOnWiU6dO1V0NoYo0bty4XHsI6iMjAwoLzZEkETTpr+ivf0nKF19kesrMNCAjA8AASRJdwfoTn7XyKix8/Gv1iaDpKREREVHdVRAEQRCEp5qYPScIgiAIgqAHETQJgOb+dELNkJ+fT0ZRv4cgCIJQBUTQJLB06VJatWrFrVu3qrsqgp6ys7N55ZVXtLa2EQRBEB6fagmagoODUSgUvPrqqzrPL1myBIVCgb+/f5XW61l09uxZpk6dSlJSkrwK9Zo1a7C0tEShUMgrUKsdPXqUzp07Y2ZmhqWlJX369OHy5cta+WZlZfHJJ5/g4+ODubk5TZo04euvv5a3HSkuJCSEfv36YW1tjUqlwtfXl5UrV1b4niIiIlAoFDRr1qzCeTzplEol8fHx7Nixg2XLllV3dQRBEJ4JoqXpGTd9+nTy8vJYvXo1Xl5eLFiwgDFjxujs9rl+/Tq9e/fm+PHjmJqaYmhoyIEDB+jSpQtRUVFyusLCQnr37s3ChQu5desWdnZ2XLt2jalTp2q1jMTFxdGtWzf27duHJEk4ODgQGhrKuHHjWLVq1WO//5rK2NiYrVu3YmZmhr+/v9a+foIgCELlE0HTM+zPP//kyJEjNG/enEGDBgFFW4L079+fF198USv9okWLePDgAePHjycxMZHExESGDx9OSkoKy5cvl9MdPnyYP//8E2dnZ/755x+ioqI4e/YsZmZmbNq0iRs3bshply9fTkpKCkOGDCEpKYm7d++ya9cuAD7//PPH/ARqNm9vb0aNGsX9+/cfqWVOEB4nMzMzrKws5X0sBaEmE0HTM2zTpk1A0WrTatOmTSMoKIjatWtrpb969SpQtNCiQqFAqVQyYcIEAM6cOSOnu379OgCDBw/G09MTgBYtWsiBmPp88TzHjh0rbwjs5+eHl5cXMTExYoB6GSZPngzA5s2bq7kmgqDbkCFDmDRpEkOGDKnuqgjCI6tRQVN2djZTp07Fw8MDY2NjXFxceO+990hKStJIN3LkSJ3jcV599VUUCgXBwcHysZSUFKZOnUq9evUwNTXF1taWrl278sMPP1BQUKBxfUhICH5+ftjY2GBmZkbXrl05f/68XnVPTEzko48+wsPDA1NTUxo0aMD06dO1usEkSWL+/Pm4ublhZmbGiy++yJEjR3SO0SkoKGDBggU0aNAAlUpFnTp1mD17ts5xQ7ocPHgQhUJBnz595GMuLi4lpn/wQHu1VfWia8XPqfe+y8rK0kibmZkJgK3tv6sCq4Oih/cJUv9cVtCk63ndvn1bK11J4+h27tyJQqFg5MiR8jF/f38UCgXffPMNs2fPplatWlhaWjJo0CCSkpJIS0tj+PDhWFlZ4eDgwPjx48nNzdXI19PTE4VCQUhICH369MHMzAx3d3eWLl0KFLXyvfDCC5iYmNCoUSO2b98uX3vnzh0MDAxo2LCh1n3069cPhULB0aNH5XIaNWrElStXiIuLK/VZCYIgCI+mRi1uOWDAAA4cOICBgQEuLi7ExcWxdu1aTp8+zdmzZzE2Lv/Kxu+88w47d+7E0NAQV1dX4uLiOHr0KEePHiUlJUVuhQkNDaVjx45kZmb+r7nZiqNHj9K9e3cuXryIt7d3qeW89957BAYGYmRkRO3atYmIiGDx4sX89ddfHDt2TE63fv16Zs+eDYCjoyMnT56kV69eOvMcPXo0P/74I1AU7MTHxzN//nzu379f5nYbaWlphIeH06hRIxwcHPR6Vo0aNeLy5ct88803LFu2jMLCQjkI8PLyktO99NJLODg4sHHjRl577TXat29PUFAQBw8epGHDhrRp00av8vRRnudVXp999hl5eXnUqlWLpKQkfvvtNwoKCkhLS+PkyZNYW1sTGxvLihUrMDMz48svv9TKo2fPnkiShJmZGVFRUUyYMIHCwkJmzZqFlZUVRkZGhIWFMWjQIC5dukSTJk3w8PCgc+fOHD16lPPnz9OyZUsAUlNTOXToEC4uLhqrwXfp0oVr165x5swZXn755Uq5d0EQBEFbtbY0BQUF0bBhQ63XxIkTtdL++eefHDhwAEdHR65du0ZUVBR37tyhYcOGXL58md9++63c5efk5LBr1y4cHByIiIggIiKCjIwM9u/fz8cff8xHH30kp503bx6ZmZmMGjWKlJQU7t27x8qVK0lLS2PBggVllrV161aCgoJISUkhMjKSW7du4ebmxvHjxzl9+rScTj025YsvviA+Pp7U1FSdO4OHhYXx448/YmNjw7lz57h79y5RUVH4+vry3XffcefOnVLrEx8fD4Cbm5tezwqKuvEUCgUrVqzAwcEBBwcHNmzYAED79u3ldDY2NuzcuZPc3Fx69+6NhYUFw4YNw8bGhl27dqFUKvUusyz6Pq+KyMvLIyoqiqioKM6cOYNKpWLHjh3cvXuXuLg4YmJi2LFjBwArVqzQapmEokA/ISGBhIQEpkyZAhQ9xwULFnDv3j2SkpJ44403KCws1Ah0hw8fDvzbhQrw3//+lwcPHvDGG29gYPDvP11XV1fg3/dUEARBeDyqvXvu+vXrWi9d1IHFiBEjaNCgAQDOzs6MGzcO0BxTo6+0tDQKCgqwsLCQv/CUSiUvvvgiS5Ys0dj9PTg4GKVSyZIlS+SxNx988AHu7u7s37+/zLKMjIzo37+/PBjSzc2NN954A0BjYPSNGzdQKpXyF6yBgQFz587Vyk/dxfjuu+/KLRG1atVi8uTJFBYW8vvvv5dan8TERAC9W5kAWrduzebNm6lfvz7Z2dnUqlULKJrJNXjwYDldXl4en3zyCfn5+RgbG1OnTh2USiXJycmVPrhb3+dVEdOmTZPvsWXLlnIL1oQJE7CwsACKgiJfX1+ysrK4e/euVh4zZszAwMAAhULBjBkz5OPjx48Hip7dtGnTAAgPD5fPv/HGG5iamrJlyxYKCwsB5C684s8ailrYoGgQvyA8aQ4dOsSvv/7KoUOHqrsqgvDIqjVoeuWVV5AkSesVEBCglVY9pVr9V7Wa+ue0tLRyl1+rVi06depEREQEHh4e2Nra0qhRI/r168dPP/2ksUliQkICBQUFWFlZoVAo5FdkZCQxMTHk5eWVWtaOHTto0aIF5ubm8rXq+yx+bVpaGhYWFnJgBv9+KRan/oL86quvNOozatQooOy96NT56xqnVJo333yT8PBwsrKy5PFBQ4YMwd7eXk6zdu1ajh07RufOnYmJiSEyMpKbN2/i4+PDhg0b9Aoy9aXv86qI4vdU/GcnJyeN4+rA8/79+6Xmof5/GxsbjYBc1/WWlpa8+uqrxMTEEBwcTEZGBgcOHMDT05O2bdtqlKEeT1WR7mlBeNwiIiIIDb0m9scUHqvs+Cwigm7Q+jGXU6PGND0Oe/bsYfXq1Zw8eZKYmBji4uLYt28f+/btIzMzkw8++AD4d8Czj4+PznxycnI0vriLu3LlCm+++Sb5+fm4urpSp04dgBJb1fRRPKDTVSd1S0hJ1IGFusWpvO7fv893332HUqlk1qxZGufUrVyffvqpHCh4eHgwefJk3nvvPQ4cOKAx+LymKWnHcX13Itf3+uHDh7N582Y2bdpEQkICOTk5vPnmm1rXqd/DygoWBUEQaork0EQuBZwjIugGUoHEG4+5vBoTNKlnUz3cBRIdHa1xHv6dvfVw61NOTo5WvpaWlnLXjtquXbvo378/e/bskYMmW1tb0tPTuXTpEiqVqlx1P3LkCPn5+UydOpXFixfLx/39/bW6kqysrMjIyCAvL08OwnR1u6hnoM2ePZt58+aVqz5Q1FpiaGjIP//8U+5rAZYtW0Z6ejpvv/029evX1zinbr0qPu4GkFccLz7TrKT3Sv2z+nxJ9H1e5flMPCl69epF7dq12b59u3xPD3fNAfJ7+HArrCAIwtMs+vAdDr21h8L8QqQC/f5ofVTVPqZJX+oZVz/99JM89kM9GBuKxtuoqVtefvnlF7nrKyEhQWOpASiaAv/aa6+xd+9ejePqJQyKB0cdO3YkLy+PSZMmyUFBVFQUc+fOLfOLV/1lnpKSIo9PkSSJsLAwrbT169enoKCAr7/+GihaXVvXNhkdO3YE4LvvvuPixYty2o0bN5Y5nkl9b61btyYmJqbcgVNGRgbLli3DwMBAq5UJoHnz5gAsWLBA7nK6c+eOfE/qMVgAvr6+AKxatUpeKmH37t3cvn0bV1fXMoMmfZ9X3bp1MTQ05NSpU3ILnyRJFZpAUFWUSiVDhw4lJSWF//73v/j4+OjcGubIkSMYGxtrddsJgiA8rZJDEzn01h4KHhRUWcAENailqX379rz44oscOHCARo0a4erqyr1798jLy8PX15fXX39dTjts2DA+/fRTfv/9d+rVq0e9evW4cOGC1qytzZs3s2PHDnbs2IGLiwv16tUjMTGR0NBQAIYOHSqn9ff35/fff+fbb79l/fr12NnZya1eubm5fPHFFyXWvVevXpiYmLB27Vp+++03HBwcSEhIIDU1VSvt+PHjGTlyJJ988gnffPMNubm5WFtba6Vr3rw5gwcPZsuWLbRo0QJnZ2fS0tLIzMzEysqKf/75Rx7EXJKePXty8uRJtm3bxsyZM0tNW9yqVatITk5m6NChOrsGx48fz08//cSRI0eoXbs2tWvX5u7duxQUFPD8889rPNfx48ezYsUKNm/ezO7du7G0tCQmJgYo6t4ri67nZWlpqZXO0tKSESNGsG7dOlq0aMELL7zAvXv3NLZ/eRINHz6cb775BkBn19zx48eJi4ujS5cuYsVl4YkiFUrkJOdAhgSZABLZidnVXa0aQ6EAVb4x2SlZ6Nnz/0y5sOgUBXkFUMXPpsYETVA0mHrOnDls3bqV2NhY7O3t8fPzY+HChRqtQlZWVuzZs4cJEyZw8eJFCgsLmTdvHocOHSIoKEhON3r0aDw9PVm5ciWnTp3i5MmTmJqa0r59ez766CONQKx58+acPHkSf39/jh8/TkJCAk2bNmXUqFF8+OGHpda7Xr167Nq1i3nz5nH16lXi4uLo3LkztWvXZt26dRpphw8fzt27d/n2229JSkqiY8eOfPbZZ3Tu3Fmru+vnn3/mueeeY8OGDdy6dQtLS0v8/Pz49NNPywyYAEaNGsXChQtZsmQJEyZMwNTUtMxrcnNz+eabbzAwMCgxqKlduzZnzpxh9uzZ7Nu3j3v37uHm5saAAQPw9/fXKKd27docPnyYTz75hL/++ouEhAQaNWrEBx98wNixY8usz8PPq3379syZM4cuXbpopQ0ICKCwsJDAwEDOnz9P3759mT59ujx4/kn0/PPPy/+vq2tu0aJFAIwZM6bK6iQI+shJzmGzz/cAFI2wzGLz3O+rs0qC8MgUkr6jV4Vqc/z4cTp37kz37t0rfdru6NGjWbduHdOnT5e/gIUnx4MHD1CpVDz33HNcunRJ49yePXvw8/PDx8eH0NBQraC6uNzcXI2xZGlpaXz/fR3ef3+smHVXTra2tjpnSgqashOz2NRABElC1XsvNVVrl4nKUqNamp4FMTExvPfeeyxevJjGjRuTkpKCv78/8O84psq0YMEC9u3bx+LFi/Hz83ssZQgVt3btWkC7lSk1NZWRI0eiVCpZtWpVqQETwMKFC7UmHcyZUzRAvrwTGwTNrYAE3VT5IhgXnj6ipekJ89lnn/H5559jYGBAnTp1iI2N5cGDB7i4uBASEvJYppUfP36c77//nrVr14pWhydI7dq1iYuLQ6VScfPmTa3ZccuWLSM3N5epU6eWmZdoaao8oqVJP6KlSaguj7OlSQRNT5jCwkICAgJYs2YNt2/fxsbGht69e/PFF1/g7u5e3dUTqpCRkRHe3t7Mnz9fXj2+sqSlpfHNN9aMHTtGBE3loFAo5KBJ/OosnXog+OnTp+RjbdqIGZ76UiiKWoJTUlLEQPCH5CRmEdhpExTqPi+6554hBgYGTJ48mcmTJ1d3VYRqVtYq84LwJFMYKDB1MKWbX3cRaFaAQqHAzNacXMMH4rk9xNTBFK9X68sLWlalGrNOkyAIgiAIAsDzE1thYGgAiqotVwRNNVBKSkp1V0GoYuI9FwRB+JddYwd6bHwJpbEShbLqIicRNNUwS5cupVWrVty6dau6qyJUkVu3btGyZUudK50LgiA8q9y6e/DywUF4vVKvygInETTVIGfPnmXq1KkkJSXJ+7g9ySIiIlAoFDq3/qhOISEh9OvXD2tra1QqFb6+vvJ2PJV1bUFBAd988w2NGjXC3Nycxo0b8+WXX8pbxaj5+/ujUCh0vtR7IioUCpKSkpgyZQrnzp179AcgCFVow4YNLFy4kA0bNlR3VYSnkF1jB7qu7cPgv0fRaXVPHvfGWGIgeA0yffp08vLyWL16NV5eXtVdnRopLi6Obt26kZKSgqWlJQ4ODoSGhjJu3DiUSmWpq5CX59opU6awZMkSDA0NcXZ2Jjw8nBkzZpCYmMhXX32llXetWrW01v5Rr+ru5eXFqlWrGDJkCDNnzuSPP/6opKchCI9fXl4eubkP5D04BeFxMHU0w6N/Xc6MOfhYyxEtTTXEn3/+yZEjR2jevDmDBg2q7urUWMuXLyclJYUhQ4aQlJTE3bt32bVrFwCff/55pVwbGRnJ0qVLcXBw4Nq1a0RGRnL+/HlMTExYunSpzllxM2fOJCwsTOM1bdo0+fzgwYN57rnnOHjwIKdOndK6XhAEQXj8RNBUQ2zatAmASZMmVXNNararV68CMHbsWPkvXz8/P7y8vIiJiSl1wLW+10ZGRjJ06FC+/PJL6tWrBxTtIdesWTPy8vK4fft2heo+ceJEoGijaUEQBKHqiaCphjh48CAKhYI+ffpoHPf09EShUHD+/Hl69eqFmZkZHh4eLFmypFz55+TkyFtqPLza8aJFi1AoFMyYMUM+dvz4cfr27YuNjQ02Nja0bduWwMBAvcpSj+N5uI4TJkxAoVDw448/ahwPCQnBz88PGxsbzMzM6Nq1K+fPn9dIM3LkSJ3XPkwd2Dy88Jn659KCJn2v7dixIxs3btTaCDg7u2iHd0tLS628g4KC8PT0lDeMDg4O1krTt29fANE9JwiCUE1E0FQDpKWlER4eTsOGDXFwcNCZpmfPnpw5cwZTU1MiIyOZOHEiQUFBepdhYmLCoEGDePDggVbws2XLFgBGjBgBQHR0NH5+fuzfvx8AMzMzTp8+zcCBA1m3bl1FbrFEoaGhdOzYkT179pCXl4eVlRVHjx6le/fuGjMInZ2d8fHxwdraulLLrwz5+fls27aNy5cv07t3b5ydnbXSBAcHc//+fUxNTfnrr7/o3bs34eHhGmmcnJxo2LAh165dIz09vaqqLwiCIPyPCJpqgPj4eADc3NxKTDNkyBBSUlJITEyUu3FWrFhRrnLUQZE6SAIICwvj0qVLvPDCCzRq1Eiux+XLlwkODiYlJYWYmBjWr18PwNdff12uMssyb948MjMzGTVqFCkpKdy7d4+VK1eSlpbGggUL5HQLFy4kLCyMAQMGVGr5j2rJkiUYGRnxxhtv8Nprr7F161ad6V588UViY2NJTEzkP//5D3l5eXzxxRda6dT7z6k/E4IgCELVEUFTDZCYmAhQYisTFI11Uk9VnzVrFgD//PNPucrp0KED9erV4/DhwyQkJADarUxqHh4edOnSRf555MiR2NnZcePGjXKVWZbg4GCUSqUcfAB88MEHuLu7yy1dTzI7Ozvq1q2LoaEhx44d49ChQxrn/f39kSSJ/fv3Y2ZmhoGBAbNnzwbg4sWLWvmpN2xWvz+CIAhC1RFBUw2gDhYePHhQYpriAZW9vT2gOT4nMDCQhg0b6nwV9/bbb1NQUMC2bdsA2Lp1K8bGxgwePFhOk52dzaRJk/Dw8ECpVMrBWnJystY6RI8qISGBgoICrKysNNYwioyMJCYm5onfn2348OHcuHGDmzdvYmhoyLBhw4iJiSn1mjp16gBF3aAPy83NBRCb7AqCIFQDsU5TDaBuXVC3OFVEamoq169fLzPd8OHD8ff3Z+vWrbRr107u8lIHYlDUOhIQEICRkRHe3t4olUoAvfIvL/VGlT4+PjrP5+Tk1Ij1X9zd3Rk0aBBLlizhyJEjDBs2rMxrdG3Sqf4MqD8TgiAIQtURQVMN4OTkhKGhYandbcnJydjY2ADIs9+KD4oeOXIkI0eOLLMsT09POnfuzPHjxwkICAC0u+bUs7dCQkJo3LixxrV37twpswx1PdPS0jSO5+TkaKW1tbUlPT2dS5cuoVKpysy7omWrf1aff5Rr//vf/3L58mVeffVVmjRpIqczNCz651a8BXDNmjWEhoYyadIk3N3dgaIlCwCtxS4Bbty4gaGhobzwpSA86bp3745KpZJbSQWhJhPdczWASqWidevWxMTElBg4LV++HEmSkCSJL7/8EoAGDRpUqLwRI0ZQWFjIhg0bcHBwoF+/fhrn1S07SUlJ8rHk5GTi4uI00pmYmABozfRStxpt27aNzMxMALKystizZ49WXTp27EheXh6TJk2SuyejoqKYO3euziCrLL6+vgCsWrVK7krcvXs3t2/fxtXVtdSgSd9rIyIimD17Nh999JEcUEVHR/Pbb0UL/NevX1/OMz4+nqVLlzJ37lzy8/MpLCxk/vz5ALRt21aj/NDQUGJjY2nTpk2lBJCCUBW8vLzw9fUVuxgITwURNNUQPXv2BJDHGj1s69at2NnZ4eDgIAdNH3/8cYXKev311zEzMwNg6NChWt1fL7/8MgCdO3fG3d0db29vateurRXEODk54eTkxK1bt/D09JQHQffu3RsfHx+uXLlC3bp16dGjB97e3iQnJ2vVxd/fHxMTE7799ltsbGxwc3PD3d0df39/5s2bJ6ebOXMmDRs2LHOtqPHjx2NlZcXmzZuxs7PD1dVVvp9PP/1UTqceAzZz5sxyXztq1Ch8fHw4cuQITk5OeHp64u3tzZ07d2jRogU9evSQ03744YfUqVOHH374gVq1auHg4MC6deswNjbWWBEcYPv27cC/nwVBEAShaomgqYYYNWoURkZGLFmyRF4ksbjt27fTuHFjMjMz8fDwYPny5fj5+VWoLEtLSwYOHAgUjXF62IwZM5g3bx4NGjQgMTGR3NxcZs2ahYeHh0Y69WKTXl5e3LlzR25xUiqVBAUF0bNnT1JTU7ly5QqjR4/mP//5j1ZZzZs35+TJk/Tv3x8TExMSEhJo2rQpAQEBzJ07V04XGxvL9evXSU1NLfXeateuzeHDh+nduzdQNNC8UaNGLF++XGPvOPUYsNjY2HJfa2FhwfHjx3nvvfews7Pj3r17eHh4MGnSJA4dOiSPAYOiLrhjx47xxhtvYGhoSE5ODp06deLQoUM8//zzcrqsrCyWL1+OkZGR1qKZgiAIQtVQSLpGmwpPpNGjR7Nu3TqmT5/OokWLgH/HEd2/f7/UriWhZps2bRpfffUVo0ePZu3atY+cX1paGmvWWDN8+FsYGYmZePpSKBTY2FiTkpKqc6C+oC0xMREzMzOysrJKXTZF0CQ+a+WXl/cAJ6eNpKamau3cUFnEQPAaZMGCBezbt4/Fixfj5+dHx44dq7tKQhU4ceIEX3/9NS4uLhoLej4qBwcwNExFqRS/BvSlUCiAfJTKdPFFpqcLF34nOzsHU1MTeSsgoWzis1Z+klS5S97oIn5b1iBOTk5s2bKF77//ntatW1d3dYQq0rp1a95++23efffdSp01N3MmvPDCR5ibm1dank87hUKBpaUHycl3xBeZnr744gTx8TnUqmVJmzZTq7s6NYb4rJVf0cSiXY+1DBE01TCdOnWiU6dO1V0NoQoZGxvz008/VXq+9+5Bfr4jBQUWlZ7306ror39nCgqyxReZnuLjldy7B6CkoEAslaEv8Vkrv/z8jMdehgiaariIiIjqroIgCIIgPBPE7DlBEARBEAQ9iKBJEARBEARBDyJoqiLBwcEaG86qXyqVikaNGvHNN9+Ifusq0rVrVxQKBSEhIdVdFUEQBKEGEUFTNfDx8ZFf5ubmhIWFMXnyZL766qvqrtoTTwQ8giAIQnURQVMVe+WVVwgLC5NfycnJbNy4EYVCwfz58yksLKzuKgqCIAiCoIMImp4Aw4YNw9fXl/T0dHmHe0EQBEEQniwiaHpC5OcXrWSq3igXihbqmj17NvXr18fExARvb2/GjBlDQkKCxrWSJLFy5UqaNWuGlZUV5ubmNG/enHnz5pGSkqKRNiEhgffeew9nZ2dUKhVNmjRh06ZNWvUpWsLfhosXL9KhQwdMTEyoX78+W7ZsASAoKIjGjRtjYmJCq1atOHr0qFYeWVlZTJkyBQ8PD4yNjalXrx5LlizRSufp6Sl3ufXp0wczMzNcXV35/PPPNcZ5de3aVS6nefPmvPrqq2U+1xMnTtC6dWtMTExo0KABmzdv1plOXYeHn5eNjc3/1kspEhERgUKhoFmzZhw6dIhmzZphYmLC888/L29I/P333+Pl5YWJiQldu3blypUrGnn6+/ujUCj45ptvmD17NrVq1cLS0pJBgwaRlJREWloaw4cPx8rKCgcHB8aPH09ubq58fffu3VEoFJw6dUoj319//RWFQsE777xT5nMRhKpy4MABUlNTOXDgQHVXRRAemVinqZqlpaWxbt06wsLCePHFFzVWfJ49ezYBAQEolUpcXFyIjY1lzZo1HDlyhMuXL2NiYgLAsmXLmDBhAgBubm5kZGQQEhJCSEgI586d47///S9QFMR07dqV0NBQDA0NcXJyIjQ0lGHDhmFkZMQbb7yhUbfU1FR69uyJoaEhKpWKGzduMHToUBITE5kwYQJOTk4oFArOnz+Pn58ft27dwtHRESgK5F5++WUOHz6MgYEBLi4uREREMHHiRPLz85kyZYrWs+jZsyeFhYVYWFgQExPDZ599Rq1atRgzZgwA7u7uGund3NxKfbb379/Hz8+P1NRUTExMyMjIYOjQoeV4d0r2999/89JLL2Fvb49SqeTy5cv079+fefPmMW3aNFxcXCgsLOTo0aO89NJL/PPPP6hUKo08PvvsM/Ly8qhVqxZJSUn89ttvFBQUkJaWxsmTJ7G2tiY2NpYVK1ZgZmbGl19+CRRtonzkyBF++eUX2rZtK+e3fft2AN58881KuUdBqAwWFhZYWVlhYWEhJrsINZ5oaapiQUFBGrPnrK2tmTlzJm+99ZZWK8j//d//sXv3buLj44mMjCQmJobmzZvzzz//EBQUJKf79ddfgaK/6KKiorh//z6XL19mzJgxrF69Wk63YcMGQkNDadmyJXfv3iU6OpozZ85gamrKzJkzddbX39+fuLg4EhMTGTx4MJIkMX78eH777Tfu3r1LXFwcnTp1IiMjg3Xr1snXHThwgMOHD+Pp6Ul4eDhRUVGEh4dTu3Zt5s2bR1ZWllZZAwYMIDExkfj4eHlQfPGWqQ0bNtClSxcALl68yIoVK0p91hs3Fm3c2Lp1a2JjY4mJieHIkSOlXqOv/Px8fv31V+7evUt8fDxdunSRW9b++usvoqKiiIyMxMfHh6ioKAIDA7XyyMvLIyoqiqioKM6cOYNKpWLHjh3yc42JiWHHjh0ArFixgoKCAgBef/11zMzM+PXXX+VjOTk57N27FwcHB3r27Fkp9ygIgiBoEkFTNSg+e87Z2ZkHDx5w8+ZNrW43hULBSy+9hJ2dHQC2traMHDkSgBs3bsjp7t+/D2h27TVt2pRVq1bh7OwsHwsODgZg3rx5cotWq1ateOONN7h58yb//POPVl0//PBDAIyMjOTWIWdnZwYMGACAlZUVH330EQDh4eFaZU2dOpW6desC4O3tzXvvvUd6ejp//vmnVlkzZszAwKDoIzl58mQsLCy4efOmHBiUl/oZjRkzBhsbG6Coi69x48YVyq84Nzc3+vfvD4C5uTnjx48HoF27dvK+gLVr12b06NGA5rNRmzZtmvw+tGzZkl69egEwYcIELCyKtjYZMGAAvr6+ZGVlcffuXaDoL/cBAwYQHx/PwYMHAdi/fz8ZGRm89tprGBqKBmRBEITHQQRNVezh2XMxMTGcOnWKS5cu0adPH/Ly8uS0R48epWPHjlhbW8stUx9//DGARrrXX38dKNqXztzcnLp169KpUycWLFhARsa/e/Gog7KXXnpJo7Vrw4YNgPaWLNbW1nIQA2Bvbw8UbRxcnIODA/Bv8Fa8rA8//FCjrHnz5uksq3j+UBQw2tvbk5eXp3EP5ZGamgogB51q6i7ER1G8rsV/1ufZVEYew4cPB+CXX34BYNu2bQAMHjy4HHchCI/funXr8Pf312iJFoSaSvxJ+gRo3bo1r7zyCps3b+b48eN0796duLg4Xn75ZdLT06lVq5bcYnT9+nWt6+fNm0eDBg3Yt28fkZGRJCUlcfLkSU6cOMGlS5fk7jv1eAJjY2O8vLy08ikeIJWmpHEJxY8X/38fHx+ttA+P76lIeU8ifZ5NZeTRs2dPXFxc2LlzJ6mpqezevRtnZ2c6d+5cvgoLwmP2ww8/cO/ePWrXrs2oUaPKfX1OYiKJp0+Tn5GBoYUFDm3aYPK/PyQEoaqJoOkJoQ5Y4uPjATh9+jTp6ekMGjSILVu2yDO4fvzxR52zo9566y3eeust+efY2FgaNGjAnj175GO2trYA7Nu3j+7duz+2eyle1rp16yr0i7IyWFtbA9qtPA93g0LRLLk7d+6QlpYmd+VB0VihJ5GBgQFDhw7l66+/5qOPPiI1NZWRI0fqHfgKwpMu9fp1wr/7jrv79iEV66JXKJW49u1Lg/ffx1rHH2SC8DiJ37BPgAMHDsgzn5o0aQIUjSGCotl1xbvirl27pnX95MmTWbZsmca09IyMDHJzczVadDp27AgUzcqLi4sDipY1+OKLL7hz506l3pO6rC+//JJbt24BRV2Ky5Yt4+LFixXOV30/+nTZ1a9fH4BVq1bJSwmcOHGC0NBQrbTq1rAff/xRPnbs2DGNZ/qkGTFiBIDcvSpmzQlPi7hjxwgeMEArYAKQCgq4u28fwQMGEHfsWDXVUHhWiZamKhYUFETDhg2Bou6WuLg4eezN4MGD5aCpbdu2ODo6sn//fuzt7alduzb3798nKSlJI7+///6blStXkpuby6effkrTpk158OABly5dIi8vT2OK/ejRo/nuu+84efIkbm5uODs7ExsbS35+Prt37+bkyZOVdp/9+/enQ4cO/Pnnn9SvXx8XFxcSEhLIzc3F09OTsLCwcnXRqTVu3Jjff/+dl19+mWHDhpU6g27YsGF89tlnnDlzBmdnZ2xtbYmPj6dWrVpyi57a+PHj+fXXX5kzZw47duzA0tJSa32lJ02TJk1o0qQJV69exc3NjXbt2lV3lQThkaVev86psWMpzMuDkrqqCwqQCgs5NXYsXQMDRYuTUGVES1M1uH79OtevXyc8PJyMjAy8vb3x9/eXWwygqHtr37599OnTB1NTU2JiYvD19WXy5Mkaefn6+nLp0iXeffddHB0dOX/+PFeuXKFBgwZ8/vnnBAQEyGktLS05ceIEY8aMwcnJiXv37uHi4sLEiRPltZwqi1Kp5MCBA0ydOhUPDw/i4uKwtbXlP//5D8HBwRUKmKBoNl7nzp3JysoiOjq61LS2trbs3r2b1q1bU1hYiImJCZs2baJRo0ZaaTt27MgPP/xA3bp1uXbtGmlpaezYsUPu4ntS1alTB6DS1p8ShMoiFRaSm5SEaWEh5oDp/34u63VtyRIK8/NLDJj+LUBCKigg/LvvquR+BAFAIdWkUbaCIGgYMGAAO3fu5MKFCzRv3lzv69LS0rC2tiYkJERe3kAom0KhwNvbm1u3btWoCQrVITcpib1t2jz2chRKJX1PnkT10GzUmk581sovIyODZs2akZqaipWV1WMpQ3TPCUINFRUVxdGjR2nQoEGZAVNubq7G+Ky0tDQAeSkIQT/qZyWeWdmq6hlJBQUknj6N20svVUl5VUV81sqvKp6VCJoEoQaaOnUqAQEBFBQU8Mknn5SZfuHChcydO1fruKen52P7i+xppmvJDkFTlqVllZVlY2qKt7d3lZVXlcRnTX/qPwYfJxE0CUINlJiYiLW1NYMGDZJXZC/NzJkzmTRpkvxzWloaderUISIiQnTPlYNCocDLy4vbt2+LLpMy5D40aeVxSsnOlmfpPi3EZ638KroQcnmIoEkQaqD169eXK71KpdI5+F6SJPELuQLEcyubkY0N/U6fZvKUKWRlZWFmZsb/ff11qdfkJCdz+KWXoLBQ73IUSiUObdo8te+H+KzpryqekwiaBEEQhEqnMDBAZW/Pyh9/1HtAs8reHrd+/XSuz6SzjP8tdPm0DQIXnlxiyYEaSr1Yo/BsEO+38Kxo8P77KJRKKGtQr0KBQqmkwfvvV03FBAERNNVIS5cupVWrVk9dH76g261bt2jZsiXLli2r7qoIwmNn7eND21WrMDAyKgqedFAolRgYGdF21SqxsKVQpUTQVMOcPXuWqVOnkpSUVCOmokZERKBQKGjWrFl1V0VDSEgI/fr1w9raGpVKha+vLytXriz1GvW9lPRSb8Hy448/lpouIiJCZ/6JiYmYmZnh4OBAdna2fFyhUJCUlMSUKVM4d+5cZT0CQXhiOXXuTNfAQFz79tUKnNRdcl0DA3ESG1QLVUyMaaphpk+fTl5eHqtXrxZTUSsoLi6Obt26kZKSgqWlJQ4ODoSGhjJu3DiUSiVjx44t9XqVSoWnp6f8c3R0NJmZmSgf+uVua2tLrVq15J/Dw8ORJEkrndqKFSvIzs5mypQpmJqayse9vLxYtWoVQ4YMYebMmfzxxx8VuGtBqB7vvfcemZmZmJubs3r1ar2vs/bx4YUlS3hu9mwSTp0iPyMDQwsLHNu2FWOYhGojWppqkD///JMjR47QvHlzBg0aVN3VqbGWL19OSkoKQ4YMISkpibt377Jr1y4APv/88zKvb9iwIWFhYfLL/n+/wNs8tPrx8OHD5TT79u1DkiRcXFxwc3PTyjM7O5uVK1eiUqkYN26c1vnBgwfz3HPPcfDgQU6dOlWR2xaEavH3339z6tQp/v777wpdr7K3x+2ll/B8803cXnpJBExCtRJBUw2yadMmAI31doTyu3r1KgBjx47FyMgIAD8/P7y8vIiJiSlx0LWRkRF169bF3d1dI6/IyEjq1q1LgwYNALCysqJu3bo4OjrK6fbs2QNA3759dXarrl+/nsTERN5++22N1qniJk6cCMDmzZvLeceCIAhCZRBBUw1y8OBBFAoFffr00Tju6emJQqHg/Pnz9OrVCzMzMzw8PFiyZEm58s/JycHGxgaVSsX9+/c1zi1atAiFQsGMGTPkY8ePH6dv377Y2NhgY2ND27ZtCQwM1Kssf39/FAqFVh0nTJigMT5ILSQkBD8/P2xsbDAzM6Nr166cP39eI83IkSN1XvswdVD08ErY6p9LCppcXV25ceOGxubG6mCoX79+8rGBAwdy48YNZs2aVWo6tcLCQr755hsUCkWpAXHfvn0BRPecIAhCNRFBUw2RlpZGeHg4DRs2xMHBQWeanj17cubMGUxNTYmMjGTixIkEBQXpXYaJiQmDBg3iwYMHWsHPli1bABgxYgRQNI7Hz8+P/fv3A2BmZsbp06cZOHAg69atq8gtlig0NJSOHTuyZ88e8vLysLKy4ujRo3Tv3l1jBqGzszM+Pj5YW1tXavmlUQdDL5Wy71VWVhbBwcEYGRnRq1cvrfM7duzg5s2bvPTSSzRq1IiIiAiCg4OJjo7WSOfk5ETDhg25du0a6enplXsjgiAIQplE0FRDxMfHA+gcD6M2ZMgQUlJSSExMlLtyVqxYUa5y1EGROkgCCAsL49KlS7zwwgs0atRIrsfly5cJDg4mJSWFmJgYeZXqr8tY9be85s2bR2ZmJqNGjSIlJYV79+6xcuVK0tLSWLBggZxu4cKFhIWFMWDAgEotvyT379/n5MmTcstXSQ4dOkROTg6dOnXCUsd+XF999RUAkydPBopm33Xr1o1t27ZppXV1dQX+/TwIgiAIVUcETTVEYmIiQImtTFA01kk9rV3dNfTPP/+Uq5wOHTpQr149Dh8+TEJCAqDdyqTm4eFBly5d5J9HjhyJnZ0dN27cKFeZZQkODkapVLJkyRJ5DNIHH3yAu7u73NJVHQ4cOEBBQQHdu3fXuUWJWmldc8eOHePMmTO0bNmy1MBLTT1OSv3eCIIgCFVHBE01hDpYePDgQYlpigdU6hldxcfnBAYG0rBhQ52v4t5++20KCgrklo6tW7dibGzM4MGD5TTZ2dlMmjQJDw8PlEqlHKwlJyeTn5//yPdbXEJCAgUFBVhZWWmsdxQZGUlMTAx5eXmVWp6+9OmaA9i7d2+J6RYvXgz828pUltzcXACMjY31rqcgCIJQOcQ6TTWEuoVB3eJUEampqVy/fr3MdMOHD8ff35+tW7fSrl07ucvLvthUX39/fwICAjAyMsLb21tee0if/MtLvV+VTwkr/+bk5MhBZVUpLCyUW7l0tSCpXb58maioKLy8vLSC09DQUPbu3Yu7uztvvPGGXuWq3//iM/MEQRCEqiGCphrCyckJQ0PDUrvbkpOTsbGxAZBnvxUfFD1y5EhGjhxZZlmenp507tyZ48ePExAQAGh3zalncIWEhNC4cWONa+/cuVNmGep6pqWlaRzPycnRSmtra0t6ejqXLl0qtRtMXyWVrf5Zfb40p0+fJjExEV9fX40lCB5WWtfcH3/8gSRJREZG6gz61OPSJkyYIB+7ceMGhoaGJS5LoC91IJqRkSF2UC8HhUJBWloa6enp4rnpqbCwUP6vmMCgP/FZK7/MzEyAx/u8JKHGaN++vQRI4eHhGsc9PDwkQJowYYJUWFgoFRYWStOnT5cAqWfPnhUq64cffpAACZAcHBykBw8eaJxv3bq1BEjHjh2TjyUlJUkmJiZS8Y9VbGysBEje3t4a1+/du1cCpKZNm0oZGRmSJElSZmam5ObmJgHS+vXr5bT9+/eXAOmDDz6QcnNzJUmSpMjISMnf31/Kzs4u97198sknEiANGTJEysvLkyRJknbt2iUBkqurq155zJo1SwKkadOmlZquQ4cOEiDt3btX69ymTZukli1bar3Uzx2QNm3aJKf/+++/JUDq0KFDOe5Wt5s3b2qUI17iJV7i9bS8bt68+ci/I0siWppqkJ49e3Ly5Em2bdvGzJkztc5v3bqVH3/8EQMDA5KTkwH4+OOPK1TW66+/zrhx48jKymLo0KFaLSEvv/wyZ86coXPnztSpUwdDQ0Oio6O1xhc5OTnh5OTErVu38PT0ZN26dfTo0YPevXvj4+PDlStXqFu3Lr6+vvz99986/xL19/fn999/59tvv2X9+vXY2dlx9+5doGiMzxdffAHAzJkzCQwMZOHChaXOoBs/fjwrVqxg8+bN7N69G0tLS2JiYgD49NNP5XSBgYHMnDmTAQMGsHDhQo08SmtBUktOTubUqVOYmprSrVs3rfNDhgxhyJAhOu937ty5BAQEaJzfvn07UPQ5eFR2dnYAREZGVukSDTVdWloaderUISoqSmudL0E38cwqRjy38ktNTcXd3V3+/fZYPLZwTKh0ERERkpGRkVSrVi0pKytLPq5uaTp58qTUvn17SaVSSR4eHtLy5csfqby33npLAqRz585pncvLy5PmzZsnNWjQQDI1NZVcXFwkf39/uS7F7du3T/Ly8pIAKTAwUD4eFhYm9ezZUzIxMZEcHR2lWbNmSePHj5dAs6VJkiTpwoULUv/+/SVbW1vJ2NhYatq0qRQQEKDRAjZixAid1+py7tw5qXfv3pKlpaVkZGQkNWrUSOt5rV+/XgKkESNGaByPjo6WAMna2lpuqdLll19+kQCpX79+ZdanuDlz5kiAFBAQIB/LzMyUHB0dJSMjI+nOnTvlyk+X1NRUCZBSU1MfOa9niXhu5SeeWcWI51Z+VfHMFJIkOktrktGjR7Nu3TqmT5/OokWLgH/HEd2/f1+v8ThCzTNt2jS++uorRo8ezdq1ax85v7S0NKytrUlNTRV/xZaDeG7lJ55ZxYjnVn5V8czEkgM1zIIFC3BxcWHx4sWcOHGiuqsjVIETJ07w9ddf4+LiorGYpyAIglC1RNBUwzg5ObFlyxbefvttWrduXd3VEapA69atefvtt9m6desjz5pTU6lUzJkzp1JmIz5LxHMrP/HMKkY8t/KrimcmuueeAqJ7ThAEQRAePxE0CYIgCIIg6EF0zwmCIAiCIOhBBE2CIAiCIAh6EEGTIAiCIAiCHkTQJAhPoeTkZIYMGYJCodA5OWDbtm288MILmJqaYmFhQefOnTl48KBWuqysLCZNmoS7uzvGxsbUrl2bd955h4SEhCq4i+qnUChKfGVkZGikDQkJoV+/flhbW6NSqfD19WXlypXVVPPqd/v2bQYNGoS9vT3GxsbUq1ePuXPnkp+fX91VeyIEBweX+Nlq1aqVRlp9/70+ja5evUqLFi1QKBS8+uqr8vGCggLmz59PvXr1MDY2xt7entdff52bN29q5VGpn8XHtmymIAjVpl69evI+TNbW1hrn9u/fL59zcnKSbG1tJUAyNDSULl++rJF28ODB8jl3d3fJ2NhYAqS2bdtKhYWFVXhH1QOQDAwMJB8fH61XZmamnO7evXuSjY2NBEiWlpaSi4uL/Iy/++67aryD6pGVlSV5e3tLgGRmZia5ublJBgYGEiBNnz69uqv3RDhy5IgESBYWFlqfrddee01OV55/r0+bGzduSCqVSr7/V155RT43Y8YM+d+nm5ubZGpqKgGSp6enxo4Zlf1ZFEGTIDyFXFxcpMWLF+sMmtSbCK9YsUKSJEkqLCyUpk6dKgHS0KFD5XTh4eESIDk6Okq3b9+WJEmS4uPjpUaNGkmA9Pvvv1fV7VQbXc9PF/UGzkOGDJG39lFvAu3i4vKYa/nkWbt2rQRIXbt2lTfkPn/+vKRSqSQjIyONgPNZpQ6aigcCuuj77/VpdPHiRcnd3V3egF79rDIzMyUjIyNJpVJJ58+flyRJkjIyMqSuXbtKgLRmzRo5j8r+LIruOUF4Cp09e5apU6fqPHf16lWMjIwYO3YsUNQFNW7cOAD+/vtvOZ36//38/PD09ATA0dGRwYMHa6V91l29ehWAsWPHyptb+/n54eXlRUxMDCkpKdVYu6qnfh7vvPMO5ubmALRo0YL27duTl5dHeHh4dVavRtH33+vTyNvbm9DQUPr06aNxPDw8nLy8PNq3b0+LFi0AMDc355133gE0n0tlfxZF0CQITyEXF5cSz6WmpmJmZoZSqZSPqfdpKv7lrv7/h/dw0pX2afbgwQP69++Pubk5Li4uTJgwgezsbI004llpEs9Df+Hh4bRq1QoTExN8fHz47rvvNM7r++/1aWRlZSUHOsWV5/NV2Z9Fw3KlFgRBeMZkZ2eza9cu3NzciImJYenSpdy/f5+ffvqpuqsmPAWuXbsmD2QODw/ngw8+wMTERG41EZ4soqVJEAShFCqVir179xIVFcXly5exsLBg48aN3Lp1q7qrJjwF3N3dCQsLIyYmRg7E586dW821EkoigiZBEIQSSJJETk4Offv2BcDX15fXX3+dwsJCLl26VM21E2qyrl27IkkSd+7cwcvLC4Dhw4dr7CUqPHlE95wg1EArVqxgxYoVWsddXV05dOhQNdSo5qnoM6xTpw4A0dHRj61uwrOrTp06REREEB0dja2tbXVXR3iICJoEoQZKTEzk+vXrWsdzcnKqoTY106M+Q0nsdS48RuLz9WQS3XOCUAP5+/sjFa2zpvGKiIgo81pra2uysrIoKCiQj6WlpQFo/GWrXklcfa60tDVRWc8wMjKSCRMmsGbNGo3rIiMjgfI9K12rsj/NnvbPTmU4fPgwEyZM4PDhwxrHH/586fvv9VlSns9XZX8WRdAkCM8YX19f8vLyWLVqFVD0F626m8rX11cjHcDu3bvlQCIxMZHNmzdrpX0aOTk58f333zN58mQuX74MFK3/sn37dgDatm0rp1U/i1WrVslbM+zevZvbt2/j6ur6zAVN6uexfv16MjMzAbhw4QInT57EyMiI+vXrV2f1ngjGxsYsXbqUSZMmkZycDMCGDRu4c+cOrq6ucjewvv9enyX169fHyMiIkydPcuHCBQAyMzP54YcfAN2/xyrts1ixdToFQagJ0LGi9d69ezW2ZbCzsytxW4ZBgwY909uofPXVVxIgKZVKqU6dOvL2C2+++aZGutjYWMnKykpso/I/WVlZkqenp7x1RfFnJ7ZR+Zefn58ESKamppKzs7POz0x5/r0+rXStnj5t2jR5G5U6depIZmZmJW6jUpmfRRE0CcJTTFfQJEmStHnzZql58+aSSqWSzMzMpI4dO0p//PGHVrr09HTpo48+klxdXSVDQ0OpVq1a0ogRI6S4uLgqqH31KywslFavXi35+vpKJiYmkqurqzRlyhQpJydHK+25c+ek3r17S5aWlpKRkZHUqFEjafny5dVQ6yfDP//8Iw0cOFCytbWVDA0NJW9vb8nf31/Ky8ur7qo9MdLT06UpU6ZInp6ekrGxsdS4cWNp7dq1Wun0/ff6tNIVNOXl5Ulz5syRvLy8JENDQ8nW1lYaOHCgdOPGDa3rK/OzqJAkMdpMEARBEAShLGJMkyAIgiAIgh5E0CQIgiAIgqAHETQJgiAIgiDoQQRNgiAIgiAIehBBkyAIgiAIgh5E0CQIgiAIgqAHETQJgiAIgiDoQQRNgiAIgiAIehBBkyAIgiAIgh5E0CQIgiAIgqAHETQJgiAIwhPgzp07DB06FGdnZywtLWnTpg1//vlndVdLKEbsPScIgiAI1Sw1NZWWLVtibW3NrFmzMDc3Z/bs2WRmZvL3339Xd/WE/zGs7goIgiAIQk2UmJiIlZUVxsbGj5zXDz/8QFxcHH/99ReOjo4AXLlyhSVLljxy3pUhJiYGFxeXcl936tQpLly4gK+vLyqViubNm6NSqSpcj/DwcJKTk7l58yaWlpa0aNECNze3CudXXqKlSRAEQRDKSZIkXnzxRZRKJTt27MDU1PSR8uvatSv29vZs375dzr9z5844OTmxbdu2yqhyhZ0/f57evXuzZMkS3n777XJdq1AoNH6+ffs2np6eFa7Lq6++SlBQkPzznDlz8Pf3r3B+5SXGNAmCoEGhUOh8qcdY/Pzzz9VdxadOcHAwCoUCExOT6q5KtSssLGTx4sUYGxujUChYtGiRVpqgoCA6deqEpaUlJiYm+Pr6smjRIvLz80vMtyLXlOarr77ijz/+4NixY0RHR1coD7XCwkLOnz9PmzZtyM7O5vLly7z11lucO3eOWbNmPVLe5alDSc/9wIEDJCcn88EHH3Djxo1KKS8zM5O3334bGxsbfHx82L9/v1aan3/+GYVCwQcffFApZVYKSRAEoRigzNeECROqu5pVbs6cOdKcOXOkqKioSs/7yJEjEiCpVKpKz7um6d69u8ZnbeHChRrn165dW+LncsiQITrzrMg1pbl3755kYmIiAdKqVasqdJ/FhYaGSoB06NAhqVWrVnLdvvrqq0fOW1+lPff8/HypQ4cOEiC9+uqr5cpXnd/t27c1jn/66aca5VlZWUnJyckaZdavX19SqVQ6/82NGDFCAqQ5c+aUqz6PSgRNgiBoUP8SCwgIkP766y/5tW/fPun111+XzwcHB1d3VauU+r7/+uuvSs9bBE3/AiRzc3PJwcFB68s7KytLsrW1lQCpTZs20o4dO6R9+/bJX6CAdPLkSY38KnJNWWbOnCkB0gsvvFAp97xx40YJkO7fvy/9+eef0saNG6UePXpIgBQYGFgpZZSltOcuSZJ05coVCZAUCoUUFhZWrnx1BU2tW7eWDAwMpK1bt0pt27aVAGnfvn3y+R9//FECpHHjxunMt7qCJtE9JwiCTm3bttV49enTh99++402bdoA8NNPP1VzDYWnUbdu3bh8+TK+vr5a506dOsX9+/dRKpXs3LmTAQMG0KdPH9avX0+TJk2Aoq6kR72mNNnZ2Xz33XcATJkypaK3qeHChQvUrVsXGxsb2rdvz7Bhw9i/fz/u7u5VNp6ptOcO0KRJE/r06YMkSQQEBDxyefHx8bi5uTFo0CBefPFFAOLi4gDIz8/n888/x8TEhJkzZz5yWZVJBE2CIJTLgAEDALh27Vo110R4Gh06dAhvb2+d52JiYgBwc3Ojdu3a8nGFQkHLli010jzKNaU5efIkKSkp1KpVi9dee01nmszMTJYsWUKHDh2wsbHBxMSEpk2bsnTpUgoLC7XSX7hwQa6LmqGhIYaGhjg7O+tdt0dR2nNXU48t2rt3b6WWLT00H+3nn3/m5s2bjBkzpkIz9h4nETQJglAu6llCDx480Di+Z88eunbtipWVFRYWFnTu3JmjR49qXV980HNqaiqjRo3C1tYWJycnBg0apPUFFhgYSLdu3bC2tsbU1JSGDRvyySefkJaWppV3ReqQlpbGu+++i729PVZWVvTv319jsGtISIjGDKB27dphY2Oj9/MqT/2BMutTXHZ2Nl988QVNmzbFzMwMKysrunXrpnNQbXnuWS05OZlRo0bh4OCAlZUVAwYMIDIyEk9PTxQKBVu2bNG6Rt/3oCQPz7YqLi8vD0DngHn1sYc/lxW5pjRHjhwBoEuXLiiVSq3zV69epWnTpkycOJFz587h5uaGi4sLf//9NxMmTGDkyJFa14SEhODk5KRx7MSJE9y6dUv+I+VxK+25q6nvOSoqips3bz5SebVq1SI6OpqtW7fKLX1OTk7k5+czf/58TE1NmTFjxiOV8VhUaWegIAhPPMoYuzNw4EAJkAYOHCgf+/bbb3UOsjU0NJQOHTqkcb16/I6RkZE8uFT9cnd3l9LS0uS08+fPL3EAb926daX4+PhKrwMgubi4yINSL168qHXe2tpar2epb/3V9TE0NJTatWtXan3UcnJypJYtW+rMW6FQSBs3bqzwPUuSJBUUFOisi5ubm2RpaSkB0ubNmzXKKM97UJYuXbpoja1Zv369BEg+Pj5a6ceMGSMB0ogRIzSOV+Sa0nTr1k0CpOXLl2udi42Nldzc3CRAmjp1qpSamiqfO3HihGRjYyMP+Fa7ceOGBEjGxsbSwoULpeDgYOm7776TnJycpLfeekvvelUWXc+9OPVnbsOGDXrlp/4MPDymadasWRqfEUtLSyk5OVketD9p0qRS8xVjmgRBeKKlpqbyf//3f+zYsQNA7ppISEhg0qRJALRq1YqtW7eyZ88e+vTpQ35+Pu+//75W8zsUtQAkJiayefNm9uzZw+DBg1m7di2WlpYA3Lx5k88++wyA559/nl9++YXDhw8za9YsjIyM5NWTH7UO8fHxbNy4kd9//51x48YBRd01q1atAqBBgwb89ddf8jUBAQEcPHiwzOdVnvqr5efnk5SUxC+//FJifdRUKhX+/v60bNmSBQsWcODAAX777TeaNm2KJElMmTJFZ1eQPvcM8Pvvv8v3/corr7Bnzx6CgoKwt7cnPT1dK9+Kvgc1TWxsLAD169fXOjd27Fiio6OZN28eixcvxsrKSj7XoUMHeXzOf//7X/n4hQsXABg6dCiLFy+mb9++rFy5kilTprBu3brHeSsVUq9ePeDf51BRM2bMYOjQoVhZWVG/fn02b96MhYUFCxYswNzcnOnTp1dGdStflYZogiA88SihZaT4q0uXLlJBQYEkSf9O53Z0dJTS09PlfHJzcyUvLy8JkE6dOiUfV7d4AFJISEiJ9fjqq68kQHJwcJDu37+vcS40NFQu/1HrcOHCBY28BwwYIAFSr169dD4XfWfPlaf+xetz6dIlvepTklu3bsl5/f333zrL0Oee1TPE6tatK+Xl5cnHY2Ji5HyKtzSV9z0oS3W2NKlbMV577TWta9Szy86ePatx/MyZM/Lzys/P13lP+/fvlwDpjTfekI/NmDFDqlOnjs701aGslqYPPvhAbknTh/qz8nBLky6rV6+WAGnatGllpq2uliaxjYogCHozMTFh+PDhfPPNNxgYFDVUX716FQBHR0f5/9WaNGnC7du3OXv2rDzrTk2hUPDcc8+VWJZ6zESXLl20xhA1atRI4+dHqUOzZs00jnXr1o3AwEDu3btXYt30UZ76F69P06ZNy1WfvLw8IiIiSE5OlltyzM3NyczMJDExUWcZ+txzfHw8AO3bt8fQ8N+vCmdnZ1xcXLTGnlX0PahpUlJSALTe082bNwOQlZVFly5ddF57//59QHN8la5B4BWRlpZGaGhoiefbtm37yGXAv/etfg6V5cGDByxYsAALCwumTp1aqXlXJhE0CYKgU0BAgMYvWgsLC7y8vDA3N9dIpx7QHBoaSrt27XTmpaspX73ycEmys7MBcHBwKLOulVkHdfdgeQYH61Ke+le0PvPnz2fhwoVkZWXpzE9X95y+ZahXyjYzM9PKw8jISOtYRd+DJ5GzszM+Pj64urpqnbOwsCAlJYWMjAyN4+oB4rGxsWXeZ/FZahcvXmT8+PGPXOcLFy7QrVu3Es9LldQ1qr5vCwuLSslP7YcffiAyMpKZM2fi4ODA5cuXGTNmDJcvX6Zu3bosXbq01PurKiJoEgRBJ/X6TGXR55dxSV/qpdFnNs/jrsOjKE/9K2Lbtm3Mnj37sZZRHk/ie1BRCxcuZOHChTrP2dvbk5KSQlJSksbxu3fvApCUlISdnZ3eZalb9GoK9X2X54+Bsjx48IAvvvgCS0tLee2rN998k7CwMGrVqsWVK1cYNGgQd+7c0RnEVyUxEFwQhEeibnkaO3YsUtEuA1qviiyGp17aQFcXU1XV4VGUp/4VsXv3bgD8/PyIj4/XuFcPD49Hzl/dJaduMSsuJydH61hVvAfqFi5d5auPGRsbP/I1pXF0dATQ2m9O3QKj63lVha5du5b43CurlQn+vW/1c6gMa9euJSoqio8//hg7OzuSkpIICwvjueeeIy4ujldeeYXExETCwsIqrcyKEkGTIAiPRD0+Z+/evVpfGA//NV4e6lk6R48eJTU1VePcpUuXNDZafVx1eBTlqX9FqMcU9erVS+sLrDK+JNXrBp06dYqCggL5+O3bt+WVm4urivdAvdBhdHS0xvgrSZI4d+6cRppHuaY0LVq0AIrWUSrO3d0dKFok8mn14MEDzpw5A1Ap47AAcnNzWbhwIdbW1vLsS/XsTFtbW+DfcVQlrW1WlUTQJAjCIxkwYADGxsZERkbSpUsXNm/ezNGjR1m8eDF169Zl8uTJFcp34MCBKJVKEhMT6datG5s3b+bIkSPMmTOHdu3aMXToUDnweFx1KM7e3h6AnTt3yl+2lVX/ivD09ATgq6++Yv369Rw/fpw//viDOXPmEBkZWeF81Tp37gxAeHg4b775Jvv27SMoKIiXXnpJZ/qqeA/atm2LjY0NBQUFDBgwgMDAQA4cOMA777zD33//DUCfPn0e+ZrSdO3aFShaLLS4119/HYBJkyZprZgtSRIXLlxg3LhxjzzBoDqdPXuW7OxsbGxstCYTVNSaNWu4e/cuEydOlIMk9Ri70NBQdu3axenTpzWOV6vHOjdPEIQah3JOrZckSVq+fHmJyxP4+vpqTLkvz+a0ixYtKjFfDw8P6d69e5Veh5KmqL/yyityfvoubqlv/StSn3PnzklGRkZa+RoYGMj/f+TIkQrfc0FBgdSxY0et/L28vKRatWppLTkgSeV7D8pS0tR39bR0Xa8hQ4bozKsi15QkPj5eUiqVEiAdP35cPp6eni61atVKztfR0VFq1aqV9Nxzz8mLgdrZ2ZWrrOpQ2pIDo0aNkkBzYduyqJ+HriUHsrOzJRcXF8nW1lZKSUnROOfj46PxPtnZ2UmZmZnyebG4pSAINda4cePYt28fvXr1wtraGpVKRZMmTVi0aBFnz54t17YjxU2fPp2dO3fK23KoVCrq16/PtGnTuHjxosbWE4+rDmoBAQF07dpVHqtU2fUvr5YtW/LHH39o3G/Tpk359ddfK2VMk4GBAbt27eLdd9/FwcEBMzMzevbsye+//y4/g4cHuz/u9wDgvffeY/v27bRv3x5zc3NUKhWNGzdm4cKFbNiwodKuKYmjoyODBg0C4Ouvv5aPW1hYcPz4cb788ktatWpFdnY2ly9fJjk5mSZNmjB79uwa3XUXFxfHL7/8AsCHH35YKXmuXr2amJgYJk2apLXQ6+bNm2nTpg1mZmb4+vry66+/VvsgcACFJD0FS7QKgiAIVcbCwoLMzEz27t1L3759q7s6Ve7y5cs8//zzKBQKDh8+LHfZPc1GjRrF+vXradWqFWfPntX7OnVgffv2bblLuTKMHDmSn376iTlz5uDv719p+ZZFtDQJgiAIOkVFRWkNeD506BCZmZlAyYt0Pu2ee+453nrrLSRJYvjw4ZW+0OOTZvv27axfvx6FQlHiUgxlOXXqFKdOnSI3N/eR6hIeHi7nVR3EOk2CIAiClnv37tG9e3fu3bvHnDlzaNmyJVevXmXevHkAtGnTplJbDmqab7/9lpMnT6JQKEhPT6+UrscnVVZWFkqlksmTJ9OzZ88K5TFkyBDg0Vucpk2bRlBQUIWvf1QiaBIEQRC0mJmZYWRkREZGhta2FlZWVnz33XfVVLMng6WlJTt27KBWrVo4OztXd3Ueq7fffhsXFxd5RuWzTIxpEgRBEHRKTU3F39+fwMBAYmJisLe3p3v37nz22Wf4+PhUd/UEocqJoEkQBEEQBEEPYiC4IAiCIAiCHkTQJAiCIAiCoAcRNAmCIAiCIOhBBE2CIAiCIAh6EEGTIAiCIAiCHkTQJAiCIAiCoAcRNAmCIAiCIOhBBE2CIAiCIAh6EEGTIAiCIAiCHkTQJAiCIAiCoAcRNAmCIAiCIOjh/wFYJB61UKr9wwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def coef_vis(lin_reg, Variables, fig_name):\n",
    "    fig = plt.figure(figsize = (4,5))\n",
    "\n",
    "    plt.grid(True, which = 'both',  zorder = -3, c = 'w')\n",
    "\n",
    "    for i in range(len(lin_reg.params.iloc[:-1])):\n",
    "        if lin_reg.params.iloc[i+1] > 0:\n",
    "            plt.scatter(np.exp(lin_reg.params.iloc[i+1]) - 1, Variables[i], s = 80, c = 'darkred', zorder = 4)\n",
    "            plt.plot([0,np.exp(lin_reg.params.iloc[i+1]) - 1], [Variables[i], Variables[i]], c = 'darkred', linewidth = 3, zorder = 4)\n",
    "        else:\n",
    "            plt.scatter(np.exp(lin_reg.params.iloc[i+1]) - 1, Variables[i], s = 80, c = 'darkblue', zorder = 4)\n",
    "            plt.plot([0,(np.exp(lin_reg.params.iloc[i+1]) - 1)], [Variables[i], Variables[i]], c = 'darkblue', linewidth = 3, zorder = 4)\n",
    "\n",
    "    Val = []\n",
    "    sig_ind = []\n",
    "    for i in range(len(Variables)):\n",
    "        Val.append(Variables[i] + '\\n(p-value: '+str(round(lin_reg.pvalues.iloc[i+1],3))+') ')\n",
    "        if lin_reg.pvalues.iloc[i+1] < 0.05:\n",
    "            sig_ind.append(i)\n",
    "\n",
    "    ax = plt.gca()\n",
    "\n",
    "    ax.set_xlabel('Percent change 100$\\cdot$($e^{\\u03B2}$ - 1) [%]', family = 'bahnschrift', fontsize = 16)\n",
    "    ax.set_yticklabels(Val, family = 'bahnschrift', fontsize = 14)\n",
    "    ax.set_xticklabels(np.arange(-100,150,50), family = 'bahnschrift', fontsize = 14)\n",
    "    ax.set_facecolor('gainsboro')\n",
    "\n",
    "    plt.axvline(0, c = 'k',ls='--', lw=2)\n",
    "\n",
    "    for i in range(len(sig_ind)):\n",
    "        plt.fill_between([-2,2],sig_ind[i]-0.5,sig_ind[i]+0.5, zorder = 3, color = 'yellow', alpha = 0.5)\n",
    "\n",
    "    plt.xlim((-1,1))\n",
    "\n",
    "    fig.savefig(fig_name, dpi = 200, bbox_inches = 'tight')\n",
    "    \n",
    "coef_vis(lin_reg, Variables, 'Figures/Regresión_LogLineal.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93ea94bf-3b40-4222-9708-b67f44f04084",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Check linear regression assumptions\n",
    "The code below checks if the assumptions for performing a linear regression are met."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56f40b7e-3e02-489b-9e51-7ad5292bf4d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relationship is homoscedastic\n",
      "No autocorrelation problem\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def check_assumptions(lin_reg, X):\n",
    "    p_val = 0.05\n",
    "\n",
    "    # Heteroscedasticidad\n",
    "    if smd.het_breuschpagan(lin_reg.resid, lin_reg.model.exog)[1] < p_val:\n",
    "        print('Relationship is not homoscedastic')\n",
    "    else:\n",
    "        print('Relationship is homoscedastic')\n",
    "\n",
    "    # Autocorrelación\n",
    "    if (durbin_watson(lin_reg.resid) < 2.5) & (durbin_watson(lin_reg.resid) > (1.5)):\n",
    "        print('No autocorrelation problem')\n",
    "    else:\n",
    "        print('There maybe autocorrelation problems')\n",
    "\n",
    "    # Linearidad\n",
    "    plt.scatter(lin_reg.predict(X), lin_reg.resid)\n",
    "    plt.ylabel('Residuals')\n",
    "    plt.xlabel('Fitted values')\n",
    "    plt.plot(lowess(lin_reg.resid, lin_reg.predict(X))[:,0], lowess(lin_reg.resid, lin_reg.predict(X))[:,1], c='r') \n",
    "    plt.title('Data linearity')\n",
    "    \n",
    "check_assumptions(lin_reg, X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06833b68-8b26-46b7-a027-e07fc6ce0bc1",
   "metadata": {},
   "source": [
    "### Feature selection\n",
    "After seeing the results of the first log-linear regression it can be seen that our regression could be performed better if we remove some features that are not being relevant in the model.\n",
    "\n",
    "Two approaches were done for the feature selection:\n",
    "1. The Variance Infllation Factor (VIF) was calculated to evaluate colinearity between features. \n",
    "    - Using VIF, `Vertical Velocity, Fault Proximity, Urban dummy and Temperature` were removed. The latter taking into account that temperature has less variation in Bogota than Precipitation, and these two were correlated.\n",
    "2. Regularization was performed using Lasso regression to evaluate the possibilit of removng more features\n",
    "    - To evaluate if more features should be removed the lambda parameter of Lasso was tuned using he Leave One Out Cross-Validation Score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "791d57f5-65c5-46da-85dd-e798ebe047f8",
   "metadata": {},
   "source": [
    "#### Variance Inflation Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a396b529-c73f-47d6-b08c-8bb9ed83a6a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.394916</td>\n",
       "      <td>Basement_dummy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.516458</td>\n",
       "      <td>Age_1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.487399</td>\n",
       "      <td>Lito_Fans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.554024</td>\n",
       "      <td>Lito_Shales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.097101</td>\n",
       "      <td>Precipitation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.938614</td>\n",
       "      <td>Temperature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.996919</td>\n",
       "      <td>Urban_dummy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.307979</td>\n",
       "      <td>Fault_proximity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.995801</td>\n",
       "      <td>Vertical_velocity</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VIF Factor           features\n",
       "0    1.394916     Basement_dummy\n",
       "1    1.516458           Age_1980\n",
       "2    1.487399          Lito_Fans\n",
       "3    2.554024        Lito_Shales\n",
       "4    4.097101      Precipitation\n",
       "5    3.938614        Temperature\n",
       "6    4.996919        Urban_dummy\n",
       "7    4.307979    Fault_proximity\n",
       "8    5.995801  Vertical_velocity"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(X.values, i+1) for i in range(X.shape[1]-1)]\n",
    "vif[\"features\"] = X.columns[1:]\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d7412f24-9247-4b4f-b190-d1bfe0a3ffba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.120172</td>\n",
       "      <td>Basement_dummy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.080660</td>\n",
       "      <td>Age_1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.252829</td>\n",
       "      <td>Lito_Fans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.067207</td>\n",
       "      <td>Lito_Shales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.230237</td>\n",
       "      <td>Precipitation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VIF Factor        features\n",
       "0    1.120172  Basement_dummy\n",
       "1    1.080660        Age_1980\n",
       "2    1.252829       Lito_Fans\n",
       "3    1.067207     Lito_Shales\n",
       "4    1.230237   Precipitation"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.drop(['Temperature', 'Fault_proximity','Vertical_velocity','Urban_dummy'], axis = 1)\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(X.values, i+1) for i in range(X.shape[1]-1)]\n",
    "vif[\"features\"] = X.columns[1:]\n",
    "\n",
    "Variables = list(vif['features'])\n",
    "\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc7c4e09-1a0a-4264-9641-c6d602876080",
   "metadata": {},
   "source": [
    "#### Lasso regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e91d2ff2-052e-485f-a2e1-0902ace93a86",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_scaled = X.iloc[:,1:].apply(lambda df :(df-np.mean(df))/np.std(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "68b7f070-2560-4a0e-8f14-8592aaceb8a3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "lin_reg = Lasso(alpha = 0.1)\n",
    "\n",
    "neg_errors = []\n",
    "\n",
    "lambdas = np.linspace(0.000000001,0.1,100+1)\n",
    "\n",
    "for i in lambdas:\n",
    "    lass_Rn = Lasso(alpha = i, fit_intercept=True)\n",
    "    scores = cross_val_score(lass_Rn, X_scaled, np.log(y), cv=30, scoring = 'neg_root_mean_squared_error')\n",
    "    neg_errors.append(scores.mean())\n",
    "    \n",
    "plt.plot(lambdas,-1*np.array(neg_errors))\n",
    "plt.ylabel('RMSE')\n",
    "plt.xlabel('Lambda')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "19ef0c84-c784-4077-bdb6-e22acfb3a02b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No further feature selection is suggested by Lasso regression\n"
     ]
    }
   ],
   "source": [
    "opt_lambda = lambdas[np.argmax(np.array(neg_errors))]\n",
    "if opt_lambda == 0.000000001:\n",
    "    print('No further feature selection is suggested by Lasso regression')\n",
    "else:\n",
    "    lass_Rn = Lasso(alpha = opt_lambda, fit_intercept=True).fit(X_scaled, np.log(y))\n",
    "    plt.stem(X_scaled.columns, lass_Rn.coef_)\n",
    "    plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f763d063-915d-4252-b58d-d456df755e29",
   "metadata": {},
   "source": [
    "### New Log-linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4265416f-1d63-43db-92a0-1819ea0fc213",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = DF_RC.iloc[:,0]\n",
    "\n",
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c080d1d9-39e0-453a-96e8-3d3392645d7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.5957690178589781\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>RC</td>        <th>  R-squared:         </th> <td>   0.350</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.215</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.589</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 May 2023</td> <th>  Prob (F-statistic):</th>  <td>0.0521</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:58:55</td>     <th>  Log-Likelihood:    </th> <td> -27.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   66.06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    24</td>      <th>  BIC:               </th> <td>   74.47</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>          <td>    5.3109</td> <td>    1.065</td> <td>    4.986</td> <td> 0.000</td> <td>    3.113</td> <td>    7.509</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Basement_dummy</th> <td>    0.3436</td> <td>    0.304</td> <td>    1.129</td> <td> 0.270</td> <td>   -0.284</td> <td>    0.972</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Age_1980</th>       <td>    0.5449</td> <td>    0.258</td> <td>    2.112</td> <td> 0.045</td> <td>    0.012</td> <td>    1.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lito_Fans</th>      <td>   -0.9890</td> <td>    0.546</td> <td>   -1.812</td> <td> 0.082</td> <td>   -2.115</td> <td>    0.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lito_Shales</th>    <td>    0.0004</td> <td>    0.700</td> <td>    0.001</td> <td> 1.000</td> <td>   -1.444</td> <td>    1.445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Precipitation</th>  <td>   -0.0099</td> <td>    0.010</td> <td>   -1.020</td> <td> 0.318</td> <td>   -0.030</td> <td>    0.010</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.010</td> <th>  Durbin-Watson:     </th> <td>   2.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.030</td> <th>  Jarque-Bera (JB):  </th> <td>   5.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.944</td> <th>  Prob(JB):          </th> <td>  0.0710</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.816</td> <th>  Cond. No.          </th> <td>    969.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     RC   R-squared:                       0.350\n",
       "Model:                            OLS   Adj. R-squared:                  0.215\n",
       "Method:                 Least Squares   F-statistic:                     2.589\n",
       "Date:                Fri, 12 May 2023   Prob (F-statistic):             0.0521\n",
       "Time:                        10:58:55   Log-Likelihood:                -27.031\n",
       "No. Observations:                  30   AIC:                             66.06\n",
       "Df Residuals:                      24   BIC:                             74.47\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "const              5.3109      1.065      4.986      0.000       3.113       7.509\n",
       "Basement_dummy     0.3436      0.304      1.129      0.270      -0.284       0.972\n",
       "Age_1980           0.5449      0.258      2.112      0.045       0.012       1.078\n",
       "Lito_Fans         -0.9890      0.546     -1.812      0.082      -2.115       0.137\n",
       "Lito_Shales        0.0004      0.700      0.001      1.000      -1.444       1.445\n",
       "Precipitation     -0.0099      0.010     -1.020      0.318      -0.030       0.010\n",
       "==============================================================================\n",
       "Omnibus:                        7.010   Durbin-Watson:                   2.292\n",
       "Prob(Omnibus):                  0.030   Jarque-Bera (JB):                5.291\n",
       "Skew:                          -0.944   Prob(JB):                       0.0710\n",
       "Kurtosis:                       3.816   Cond. No.                         969.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg = sm.OLS(np.log(y), X).fit(maxiter=1000000)\n",
    "\n",
    "RMSE = np.sqrt(np.sum((lin_reg.predict(X)-np.log(y))**2)/30)\n",
    "\n",
    "print('RMSE:', RMSE)\n",
    "\n",
    "lin_reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d2340184-a61d-4319-83bf-3b125adf6c63",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef_vis(lin_reg, Variables, 'Figures/Regresión_LogLineal_withFeatureSel.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "66be12b3-d832-4a4d-8f5e-132d9f5609ea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relationship is homoscedastic\n",
      "No autocorrelation problem\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_assumptions(lin_reg, X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7bdc023-9a9f-459c-8b2d-d9c66afbe22a",
   "metadata": {},
   "source": [
    "### Model evaluation with Leave One Out Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e4726476-28b0-4a92-a3f8-153644fe9805",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([X, np.log(y)], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dbeb6156-3e0c-420c-a5d4-d74d36d3fb05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.51742327434803 50.723929522241896\n"
     ]
    }
   ],
   "source": [
    "errors = []\n",
    "# LOOCV\n",
    "for i in range(len(df)):\n",
    "    \n",
    "    # splitting our dataset into train and test datasets.\n",
    "    train = pd.concat([df.iloc[:i], df.iloc[(i+1):]])\n",
    "    test = pd.DataFrame(df.iloc[i]).T\n",
    "    lin_reg = sm.OLS(train.RC, train.iloc[:,:-1]).fit(maxiter=1000000)\n",
    "    \n",
    "    RMSE = np.sqrt(np.sum((np.exp(lin_reg.predict(test.iloc[:,:-1])) - np.exp(test.RC))**2)/1)\n",
    "    errors.append(RMSE)\n",
    "    \n",
    "print(np.mean(errors), np.std(errors))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c906b2e1",
   "metadata": {},
   "source": [
    "## Apply models to Household data\n",
    "With the code below the log-linear regression fitted is apply to the dataset from Bogota's Cadaster. The code receives a zipped txt file and returns a csv, a geojson and a raster of the resulting estimations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad329b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unzip IndVarModel file\n",
    "with zipfile.ZipFile(\"Dataset for regression/Houses_for_Rn_estimation_processed_3116.zip\", 'r') as zip_ref:\n",
    "    zip_ref.extractall(\"Dataset for regression\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13616435-0f81-49ac-96a5-fd62355d4d27",
   "metadata": {},
   "source": [
    "Data is clustered spatially to minimize computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d46a77c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_RnModel = pd.read_table(\"Dataset for regression/Houses_for_Rn_estimation_processed_3116.txt\", delimiter = ',')\n",
    "\n",
    "#pre-Processing for model implementation\n",
    "df_RnModel = df_RnModel.dropna()\n",
    "df_RnModel['const'] = np.ones(len(df_RnModel))\n",
    "\n",
    "x_range = df_RnModel['X'].max() - df_RnModel['X'].min()\n",
    "y_range = df_RnModel['Y'].max() - df_RnModel['Y'].min()\n",
    "\n",
    "width = 300\n",
    "height = int(width * (y_range/x_range))\n",
    "\n",
    "x_iter = np.linspace(df_RnModel['X'].min(), df_RnModel['X'].max() + x_range/width, width)\n",
    "y_iter = np.linspace(df_RnModel['Y'].min(), df_RnModel['Y'].max() + y_range/height, height)\n",
    "\n",
    "df_RnModel['Cluster'] = np.zeros(len(df_RnModel))\n",
    "\n",
    "k = 0\n",
    "for i in range(len(x_iter)-1):\n",
    "    for j in range(len(y_iter)-1):\n",
    "        k += 1\n",
    "        df_RnModel.loc[(df_RnModel.X >= x_iter[i])&(df_RnModel.X < x_iter[i+1])&(df_RnModel['Y'] >= y_iter[j])&(df_RnModel['Y'] < y_iter[j+1]), 'Cluster'] = k\n",
    "              \n",
    "df_RnModel = df_RnModel.groupby('Cluster').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f6e100",
   "metadata": {},
   "source": [
    "### Log-Linear regression\n",
    "Take the clusterized tabular data with independent variables, predict the Radon concentrations and export a csv with the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5ba42c04",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_RnModel_reg = df_RnModel[list(X.columns)]\n",
    "\n",
    "df_RnModel['LinReg'] = np.exp(lin_reg.predict(df_RnModel_reg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a86e85b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_RnModel[['X','Y','LinReg']].to_csv('Regression results/LinReg_model_results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6fa0a0a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OID_</th>\n",
       "      <th>Fault_proximity</th>\n",
       "      <th>Basement_dummy</th>\n",
       "      <th>Lito_Fans</th>\n",
       "      <th>Lito_Shales</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Urban_dummy</th>\n",
       "      <th>Vertical_velocity</th>\n",
       "      <th>Age_1980</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>const</th>\n",
       "      <th>LinReg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>2.707500e+04</td>\n",
       "      <td>2.707500e+04</td>\n",
       "      <td>27075.0</td>\n",
       "      <td>27075.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>307172.717485</td>\n",
       "      <td>8452.579936</td>\n",
       "      <td>0.010090</td>\n",
       "      <td>0.224700</td>\n",
       "      <td>0.062290</td>\n",
       "      <td>105.276750</td>\n",
       "      <td>14.471483</td>\n",
       "      <td>0.934655</td>\n",
       "      <td>2.447333</td>\n",
       "      <td>0.578726</td>\n",
       "      <td>9.967256e+05</td>\n",
       "      <td>1.005529e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.680769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>166010.476394</td>\n",
       "      <td>2910.257474</td>\n",
       "      <td>0.072528</td>\n",
       "      <td>0.415684</td>\n",
       "      <td>0.241194</td>\n",
       "      <td>18.091099</td>\n",
       "      <td>0.307099</td>\n",
       "      <td>0.245794</td>\n",
       "      <td>0.532761</td>\n",
       "      <td>0.392475</td>\n",
       "      <td>4.909564e+03</td>\n",
       "      <td>7.738842e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.062044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.291032</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.296017</td>\n",
       "      <td>13.401693</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.390588</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.846713e+05</td>\n",
       "      <td>9.822120e+05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.467579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>156316.183333</td>\n",
       "      <td>6333.022815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.933632</td>\n",
       "      <td>14.305622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.081759</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>9.933950e+05</td>\n",
       "      <td>1.000595e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.374732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>328553.227273</td>\n",
       "      <td>8265.938607</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>103.894888</td>\n",
       "      <td>14.442628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.238900</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>9.968176e+05</td>\n",
       "      <td>1.004962e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>84.950584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>450847.875000</td>\n",
       "      <td>10435.165545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>113.280541</td>\n",
       "      <td>14.672647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.813727</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>1.000251e+06</td>\n",
       "      <td>1.011389e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>111.560191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>551514.916667</td>\n",
       "      <td>15580.113111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>185.895126</td>\n",
       "      <td>15.722256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.619085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.008409e+06</td>\n",
       "      <td>1.025891e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>263.424310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                OID_  Fault_proximity  Basement_dummy     Lito_Fans  \\\n",
       "count   27075.000000     27075.000000    27075.000000  27075.000000   \n",
       "mean   307172.717485      8452.579936        0.010090      0.224700   \n",
       "std    166010.476394      2910.257474        0.072528      0.415684   \n",
       "min         8.000000        12.291032        0.000000      0.000000   \n",
       "25%    156316.183333      6333.022815        0.000000      0.000000   \n",
       "50%    328553.227273      8265.938607        0.000000      0.000000   \n",
       "75%    450847.875000     10435.165545        0.000000      0.000000   \n",
       "max    551514.916667     15580.113111        1.000000      1.000000   \n",
       "\n",
       "        Lito_Shales  Precipitation   Temperature   Urban_dummy  \\\n",
       "count  27075.000000   27075.000000  27075.000000  27075.000000   \n",
       "mean       0.062290     105.276750     14.471483      0.934655   \n",
       "std        0.241194      18.091099      0.307099      0.245794   \n",
       "min        0.000000      60.296017     13.401693      0.000000   \n",
       "25%        0.000000      92.933632     14.305622      1.000000   \n",
       "50%        0.000000     103.894888     14.442628      1.000000   \n",
       "75%        0.000000     113.280541     14.672647      1.000000   \n",
       "max        1.000000     185.895126     15.722256      1.000000   \n",
       "\n",
       "       Vertical_velocity      Age_1980             X             Y    const  \\\n",
       "count       27075.000000  27075.000000  2.707500e+04  2.707500e+04  27075.0   \n",
       "mean            2.447333      0.578726  9.967256e+05  1.005529e+06      1.0   \n",
       "std             0.532761      0.392475  4.909564e+03  7.738842e+03      0.0   \n",
       "min             1.390588      0.000000  9.846713e+05  9.822120e+05      1.0   \n",
       "25%             2.081759      0.153846  9.933950e+05  1.000595e+06      1.0   \n",
       "50%             2.238900      0.700000  9.968176e+05  1.004962e+06      1.0   \n",
       "75%             2.813727      0.976744  1.000251e+06  1.011389e+06      1.0   \n",
       "max             4.619085      1.000000  1.008409e+06  1.025891e+06      1.0   \n",
       "\n",
       "             LinReg  \n",
       "count  27075.000000  \n",
       "mean      85.680769  \n",
       "std       33.062044  \n",
       "min       21.467579  \n",
       "25%       59.374732  \n",
       "50%       84.950584  \n",
       "75%      111.560191  \n",
       "max      263.424310  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_RnModel.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37797e63-c8ec-4b89-94b1-1cd29301ea12",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Rasterize the results\n",
    "1. Create geodataframe with XY coorinates of clusters and export to geojson\n",
    "2. Rasterize geojson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9728a97-bf69-41ff-b6fa-bcdcf8aa1a2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Program Files (x86)\\\\MapWindow\\\\PROJ_NAD'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.environ['PROJ_LIB']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b1b9a520-45bb-48af-b0bd-6d08077518f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gpd.GeoDataFrame(df_RnModel['LinReg'], geometry=gpd.points_from_xy(df_RnModel.X, df_RnModel.Y))\n",
    "gdf = gdf.set_crs('EPSG:3116')\n",
    "gdf.to_file('Regression results/Rn_estimations.geojson', driver=\"GeoJSON\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "14f92a8e-ff3b-4918-b4c5-45585d87c728",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_points = ogr.Open('Regression results/Rn_estimations.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a126e2a5-17b4-4b38-8b19-ac0cd8227985",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_points\n",
    "lyr = ds_points.GetLayer() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ddb610d6-3906-40b4-a511-4140986f198a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROJCS[\"MAGNA-SIRGAS / Colombia Bogota zone\",\n",
      "    GEOGCS[\"MAGNA-SIRGAS\",\n",
      "        DATUM[\"Marco_Geocentrico_Nacional_de_Referencia\",\n",
      "            SPHEROID[\"GRS 1980\",6378137,298.257222101,\n",
      "                AUTHORITY[\"EPSG\",\"7019\"]],\n",
      "            AUTHORITY[\"EPSG\",\"6686\"]],\n",
      "        PRIMEM[\"Greenwich\",0,\n",
      "            AUTHORITY[\"EPSG\",\"8901\"]],\n",
      "        UNIT[\"degree\",0.0174532925199433,\n",
      "            AUTHORITY[\"EPSG\",\"9122\"]],\n",
      "        AUTHORITY[\"EPSG\",\"4686\"]],\n",
      "    PROJECTION[\"Transverse_Mercator\"],\n",
      "    PARAMETER[\"latitude_of_origin\",4.59620041666667],\n",
      "    PARAMETER[\"central_meridian\",-74.0775079166667],\n",
      "    PARAMETER[\"scale_factor\",1],\n",
      "    PARAMETER[\"false_easting\",1000000],\n",
      "    PARAMETER[\"false_northing\",1000000],\n",
      "    UNIT[\"metre\",1,\n",
      "        AUTHORITY[\"EPSG\",\"9001\"]],\n",
      "    AXIS[\"Northing\",NORTH],\n",
      "    AXIS[\"Easting\",EAST],\n",
      "    AUTHORITY[\"EPSG\",\"3116\"]]\n"
     ]
    }
   ],
   "source": [
    "spatref = osr.SpatialReference()\n",
    "spatref.ImportFromEPSG(3116)\n",
    "\n",
    "print(spatref)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eac81e29-cdca-4f52-84bb-3eccc1d0a982",
   "metadata": {},
   "outputs": [],
   "source": [
    "driver = gdal.GetDriverByName('GTiff')\n",
    "\n",
    "spatref = osr.SpatialReference()\n",
    "spatref.ImportFromEPSG(3116)\n",
    "wkt = spatref.ExportToWkt()\n",
    "\n",
    "outfn = 'Regression results/LogLinear_estimations.tif'\n",
    "nbands = 1\n",
    "nodata = 0\n",
    "xres = 100\n",
    "yres = -100\n",
    "\n",
    "xmin = gdf.geometry.x.min()\n",
    "xmax = gdf.geometry.x.max() \n",
    "ymin = gdf.geometry.y.min()\n",
    "ymax = gdf.geometry.y.max()\n",
    "dtype = gdal.GDT_Float64\n",
    "\n",
    "xsize = abs(int((xmax - xmin) / xres))\n",
    "ysize = abs(int((ymax - ymin) / yres))\n",
    "\n",
    "ds = driver.Create(outfn, xsize, ysize, nbands, dtype)\n",
    "ds.SetProjection(wkt)\n",
    "ds.SetGeoTransform([xmin, xres, 0, ymax, 0, yres])\n",
    "ds.GetRasterBand(1).Fill(0)\n",
    "ds.FlushCache()\n",
    "\n",
    "gdal.RasterizeLayer(ds, [1], lyr, options=['ATTRIBUTE=LinReg', 'ALL_TOUCHED=TRUE'])\n",
    "ds.GetRasterBand(1).SetNoDataValue(nodata) \n",
    "ds = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1741a97",
   "metadata": {},
   "source": [
    "## Assessment of RC estimations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8dfae8e4-ea1c-466a-909d-04dd23b84239",
   "metadata": {},
   "outputs": [],
   "source": [
    "Geo_mean = np.exp(np.mean(np.log(df_RnModel.LinReg)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c340cbaf-7c0e-4a10-a971-e268d3edcef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.28829834475358\n"
     ]
    }
   ],
   "source": [
    "print(Geo_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ec97dcf6-16d2-44c0-a6b7-74e63e4a1c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create figure\n",
    "fig = plt.figure(figsize = (13,8))\n",
    "\n",
    "# send grid to back\n",
    "plt.grid(axis='y', zorder = -1)\n",
    "\n",
    "# Compute histogram\n",
    "h = plt.hist(df_RnModel.LinReg, bins = np.arange(0,450,25), \n",
    "             edgecolor = 'k', color = 'slategray', zorder = 4)\n",
    "\n",
    "# compute median calculation\n",
    "median = df_RnModel.LinReg.median()\n",
    "\n",
    "# Edit axis\n",
    "ax = plt.gca()\n",
    "ax.set_facecolor('gainsboro')\n",
    "\n",
    "# Edit figure\n",
    "plt.ylabel('Number of estimated values',fontdict = font, fontsize = 20)\n",
    "plt.xlabel('Estimated residential Rn concentration [Bq/m$^3$]',fontdict = font, fontsize = 20)\n",
    "plt.xticks(np.arange(12.5,420,25))\n",
    "ax.set_xticklabels(['<25','25-50','50-75', '75-100', '100-125', '125-150', '150-175', '175-200', '200-225','225-250','250-275',\n",
    "                    '275-300','300-325','325-350','350-375','375-400','400-425'], fontdict = font, fontsize = 14, rotation = 90)\n",
    "ax.set_yticklabels(np.arange(0,220000,25000), fontdict = font, fontsize = 16)\n",
    "plt.xlim((0,300))\n",
    "\n",
    "\n",
    "#Create second yaxis\n",
    "ax2 = ax.twinx() \n",
    "ax2.set_ylim((0,1.01))\n",
    "\n",
    "# Plot accumulated prcentage of measurements.\n",
    "plt.plot(np.arange(12.5,420,25),np.cumsum(h[0])/len(df_RnModel), zorder=5, c = 'k', ls ='--')\n",
    "ax2.set_yticklabels(['0%','20%', '40%', '60%', '80%', '100%'], fontdict = font, fontsize = 16)\n",
    "ax2.set_ylabel('Accumulated percentage of estimated values [%]', fontdict = font, fontsize = 20)\n",
    "\n",
    "# Plot Geometric mean value\n",
    "ax2.axvline(Geo_mean, c = 'darkred',ls='-.')\n",
    "ax2.text(Geo_mean, 0.94,'Geometric mean:\\n'+str(round(Geo_mean,2))+' Bq/m$^3$', va='center', ha='center', bbox = dict(facecolor='w', alpha=0.9), family = font['family'], fontsize = 18, color = 'darkred')\n",
    "\n",
    "\n",
    "# Save figure histogram\n",
    "fig.savefig('Figures/Estimated_Rn_Histogram.png', dpi = 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "01289bbf-03f2-4d83-8251-d17822167ea9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_RnModel = pd.read_table(\"Dataset for regression/Houses_for_Rn_estimation_processed_3116.txt\", delimiter = ',')\n",
    "#pre-Processing for model implementation\n",
    "df_RnModel = df_RnModel.dropna()\n",
    "df_RnModel['const'] = np.ones(len(df_RnModel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "02d1b00d-1fc9-41f4-9264-8132a08a8fd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_RnModel_reg = df_RnModel[list(X.columns)]\n",
    "\n",
    "df_RnModel['LinReg'] = np.exp(lin_reg.predict(df_RnModel_reg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1d8f365b-7f0c-4c0d-a391-5407f4c17e21",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3209166560907954"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_RnModel[df_RnModel['LinReg'] > 100])/(len(df_RnModel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3e25545e-da05-4f4b-ade7-06b62d66bc23",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "513323.2"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_RnModel[df_RnModel['LinReg'] > 100])*2.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa34d1c4-dc7e-4c6c-b88f-a709dadd9a22",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}